The objective of this study is to develop a high-precision optical interferometric phase demodulation method that does not require preset phase shifts and can accurately recover phase information from only three interferograms. The proposed method is designed to overcome the inherent limitations of conventional random phase-shifting algorithms， which typically suffer from large errors， instability， and restricted accuracy improvements， particularly when the number of interference fringes is limited. At the same time， the method ensures robustness under varying fringe types， Signal-to-Noise Ratios （SNR）， and fringe numbers. In this work， a novel three-frame random phase-shifting phase demodulation algorithm is proposed， referred to as the VU&LEF method， which integrates the VU algorithm with ellipse-fitting correction. First， the VU algorithm is applied to three randomly phase-shifted interferograms to extract two orthogonal components of the interferometric signal. In practice， due to noise interference and the limited number of frames， these components are not perfectly orthogonal， and their distribution often forms an ellipse rather than an ideal circle. To correct this deviation， ellipse-fitting is introduced to estimate the ellipse coefficients. A subsequent normalization procedure maps the ellipse into a circle， thereby restoring strict orthogonality between the two components and significantly reducing systematic errors in the subsequent phase calculation. This optimization enables high-precision phase recovery even with only three interferograms， effectively overcoming the main drawbacks of the conventional VU method. To evaluate the performance of the proposed approach， extensive numerical simulations and experimental validations were conducted under diverse conditions. The results demonstrate that the VU&LEF method consistently outperforms the conventional VU algorithm， achieving significantly improved phase accuracy. Even under low-SNR conditions， the proposed method maintains stable phase recovery， whereas the traditional approach exhibits severe degradation. To further assess computational efficiency， the processing time of the VU&LEF method was compared with that of the conventional VU method and the AIA algorithm. Although the ellipse-fitting step introduces a slight additional computational cost， the proposed method remains more efficient than the AIA algorithm while simultaneously offering superior phase accuracy. Furthermore， the robustness of the proposed method was verified using representative open， closed， and complex fringe patterns with significant background fluctuations. The results clearly indicate that the method effectively corrected elliptical distributions into ideal circular forms， thereby reducing measurement errors. In contrast， the conventional VU method exhibited large phase errors and severe distortions in regions with strong background interference， whereas the VU&LEF method substantially mitigated these errors through ellipse correction. Additional tests under varying frame numbers confirmed that the proposed method yields stable and reliable phase recovery with significantly reduced error fluctuations. These findings further indicate that phase correction is indispensable when performing demodulation with only three interferograms， highlighting the wide applicability of the method in practical interferometric measurements. From an application perspective， requiring only three interferograms greatly reduces data acquisition time compared with conventional multi-frame methods. The additional computational cost introduced by ellipse-fitting correction is negligible compared with the measurement time saved. This combination of high efficiency and accuracy makes the method particularly suitable for real-time and high-speed measurement scenarios， such as dynamic surface profiling， deformation monitoring， in-situ industrial inspection， and other precision optical metrology tasks. In conclusion， the proposed three-frame random phase-shifting phase demodulation method with ellipse-fitting correction does not require the phase shifts of interferograms to be predefined or equally spaced， and thus constitutes a true random phase-shifting technique. It inherits the advantage of the VU method in not requiring prior calibration of phase steps， while enabling high-precision phase recovery from minimal data. Moreover， it demonstrates strong robustness against noise， fringe variability， and complex background interference. Both simulation and experimental results verify that the proposed method outperforms conventional approaches in terms of accuracy， stability， and computational efficiency.

Binocular stereo vision has become a mainstream three-dimensional imaging solution in medical imaging analysis and industrial inspection because of its low cost， passive sensing and real-time processing capability. However， conventional stereo matching algorithms exhibit intrinsic limitations when confronting highly reflective and weakly textured objects such as polished ceramics， metals or water-like surfaces. Specular highlights saturate the sensor and obscure surface micro-structure， while the lack of natural texture causes a reduction in reliable feature points， resulting in mismatches， depth discontinuities and holes in reconstructed point clouds. Existing methods can be broadly categorized into active feature augmentation， which relies on projected patterns， added particles or structured light， and passive algorithmic optimization， which refines feature detection or incorporates learning-based priors. Active schemes increase system complexity， cost and sensitivity to environmental illumination， whereas passive schemes remain vulnerable to strong glare and texture scarcity， and often depend on prior knowledge of material properties. Polarization imaging， which is sensitive to surface reflection mechanisms and material characteristics， offers a promising avenue， yet current polarization-stereo fusion strategies still suffer from reflection-model inaccuracies， normal ambiguity， dependence on refractive index， and error accumulation during normal integration. These limitations motivate the present work， which aims to develop a polarization-augmented binocular stereo framework that alleviates specular interference and texture deficiency without introducing additional active devices.

The proposed method constructs a polarization-fused binocular stereo vision pipeline that combines physically based reflection decomposition with multimodal feature enhancement and conventional triangulation. A stereo pair of polarization-sensitive cameras acquires synchronized images at four polarization angles （0°， 45°， 90° and 135°）， and the system is calibrated using a polarization chessboard and Zhang's method to obtain intrinsic and extrinsic parameters and to ensure accurate epipolar rectification. At the image-formation level， the observed intensity at each polarization angle is modeled as a superposition of specular and diffuse components. Multi-angle intensities are assembled into an observation matrix and approximated as low rank via singular value decomposition， which factorizes the data into coefficient and reflection matrices. An unknown transformation matrix is then optimized by minimizing the Pearson correlation between specular and diffuse estimates， yielding a separation in which diffuse components retain texture while specular energy is strongly attenuated. In parallel， Stokes-parameter analysis of the four polarization channels provides the degree and angle of polarization， from which azimuth and zenith angles of surface normals are inferred under an assumed refractive index. Intensity-based SURF descriptors are extracted on the glare-suppressed diffuse image， polarization-normal descriptors are computed from the normal map， and both standardized descriptors are concatenated into a joint feature representation that guides stereo matching， disparity computation and depth estimation.

Extensive experiments on highly reflective ceramic and metallic targets demonstrate that the proposed polarization-enhanced stereo framework markedly improves both feature richness and three-dimensional reconstruction quality. Using a dual MER2-502-79U3M polarization camera system with precise baseline control and synchronized acquisition of four-angle polarization sequences， the authors first examine feature detection on ceramic samples with strong specular highlights. SURF-based analysis shows that， before enhancement， only a few dozen stable feature points are available in each region， whereas after specular-diffuse separation and polarization-based feature augmentation， the number of keypoints increases by roughly an order of magnitude and feature coverage improves correspondingly. Gray-level histograms confirm that the proportion of pixels in high-intensity highlight ranges drops sharply after processing， indicating effective suppression of saturated regions and recovery of latent texture. Depth profiles along selected cross-sections reveal that original stereo reconstructions exhibit pronounced oscillations and artificial depressions in glare-dominated areas， while the enhanced method yields smooth， continuous depth curves that closely approximate the true object geometry. Quantitative distance measurements between reconstructed and physically measured point pairs show absolute errors below 5 mm and relative errors below about 0.5. On metallic heater targets， the method fills the vast majority of point cloud holes， elevates surface completeness to almost full coverage， reduces highlight-dominated pixel proportions， and increases local feature density by several tens of percent in difficult regions.

Comparative evaluation against three representative baselines further highlights the advantages and limitations of the proposed approach. Traditional binocular stereo vision， although computationally efficient， exhibits the lowest structural similarity because it relies solely on intensity texture and is easily disrupted by glare and weak-patterned surfaces. Pure polarization-based three-dimensional imaging improves detail recovery by exploiting polarization-derived normals， yet its accuracy is constrained by unknown or spatially varying refractive indices and by error accumulation during normal integration. A hybrid method that fuses polarization with deep neural networks achieves higher reconstruction quality by resolving angular ambiguities， but requires substantial computational resources and long inference times， which challenge real-time deployment. In contrast， the proposed specular–diffuse separation and polarization-augmented stereo matching strategy achieves the highest structural similarity index while maintaining moderate runtime， thereby realizing a favorable balance between precision and efficiency without resorting to additional active projection hardware. Overall， the work shows that incorporating polarization cues at the level of reflection modeling and feature description can effectively overcome the dual challenges of high specular reflection and texture scarcity， significantly reducing point cloud hole rates and enhancing geometric fidelity. The method therefore offers a promising solution for high-precision， passive three-dimensional measurement of highly reflective， weakly textured objects in industrial inspection and related application scenarios.

The high penetrating power， high resolution， and non-invasive nature of X-rays make them widely applicable in medical examinations， industrial flaw detection， and security screening. While X-ray Computed Tomography （CT） technology is extensively used and relatively mature， its significant radiation exposure， lengthy data acquisition time， and intricate mechanical structure hinder broader adoption. Furthermore， CT can not achieve three-dimensional imaging for certain flat samples. Computed Laminography （CL）， a derivative of CT technology， effectively addresses this limitation. However， prolonged exposure times and complex operational procedures still result in extended imaging durations and reduced efficiency， preventing in-situ observation. Consequently， there is an urgent need to develop a new technology capable of achieving high-efficiency， high-quality three-dimensional imaging of flat samples. Unlike visible light imaging， X-ray transmission methods yield images of relatively poor quality relative to the demands of stereoscopic imaging algorithms. Stereo imaging is a promising approach， as it requires only two images captured from different angles to achieve depth reconstruction. This method holds potential for enabling high spatio-temporal resolution stereoscopic imaging when combined with synchrotron radiation sources. Existing denoising algorithms prove ineffective for these images， failing to achieve satisfactory depth information recovery. Consequently， stereoscopic imaging techniques—well-established in the visible light domain—have not been successfully applied to X-ray imaging. This study focuses on the lack of dedicated denoising algorithms in the field of X-ray stereo vision imaging， and proposes a Gray Value Remapping Denoising Method （GVRM）. This method employs the NlMD function from the OpenCV open-source library during the preprocessing stage to perform initial noise reduction. It then enhances edge features using the Laplacian operator and further processes anomalous pixels with the NlMD function after adjusting its parameters. Subsequently， in the feature segmentation stage， a radial attenuation model is established based on Lambert-Beer law， and the Otsu's method is applied to achieve adaptive threshold segmentation， which supports multilevel gray-scale quantization. Finally， a normalized inter-correlation algorithm is used in the stereo matching stage to calculate the parallax field and generate high-precision parallax maps. In order to evaluate the advantages and disadvantages of the algorithms， the simulation of embedded wire in the pre-slab material and the nano-resolution low-latency imaging experiments at the synchrotron nanowire station were carried out， and the parallax calculations were performed on the images obtained by using the different processing methods， and the results were evaluated by using the standard parallax. Additionally， due to beam instability in synchrotron radiation experiments， which can cause spot jitter or brightness fluctuations， the jitter of the beam and various components may also shift the position of X-rays incident on the sample. This results in inconsistent projection positions at different angles， leading to image misalignment. Consequently， image registration is required. A simulated plate-like material embedded with filamentous objects was projected at two distinct angles， with added noise. The depth information of the filamentous objects was recovered， achieving a peak Signal-to-noise Ratio （SNR） of 17.28 dB and a Structural Similarity Index （SSI） of 0.92， outperforming depth information obtained from existing denoising algorithms. The experiments based on the BL18B at the Shanghai Synchrotron Radiation Facility show that the algorithm enables the parallax map to reach a peak signal-to-noise ratio of 40.54 dB， a root-mean-square error of 2.4， and a structural similarity index of 98.25%. The method breaks through the performance bottleneck of existing algorithms in complex noise scenarios， and achieves nano-resolution stereo vision imaging using synchrotron radiation， which significantly reduces the noise level of the parallax map of X-ray stereo vision imaging， improves the pixel-point parallax accuracy， and provides an effective solution for high spatial-resolution three-dimensional imaging of nano-scale samples. The success of this algorithm verifies the feasibility of the image noise reduction strategy based on the laws of physics in the field of X-ray stereo imaging， and provides a new paradigm for high-precision X-ray stereo vision imaging.

Infrared sensors capture the thermal radiation emitted from the surface of objects to create images. However， infrared images are prone to noise interference and are limited in capturing fine details and texture information. In contrast， visible sensors capture reflected light from the surface of objects， providing rich details and texture information that align with human visual perception. However， the quality of visible images can be unstable due to factors such as lighting conditions. Therefore， effectively fusing infrared and visible images to generate high-quality images is an important research direction.

Currently， image fusion methods for infrared and visible images can be broadly categorised into traditional fusion strategies and deep learning-based fusion strategies. Traditional methods struggle to effectively integrate the feature information from the two modalities， especially in complex fusion scenarios. Deep learning-based fusion methods， such as ResNet and DenseNet， can better handle multi-modal information， but they involve complex training processes and high computational overhead. To address these challenges， this paper proposes an infrared and visible image fusion method that combines CNN-Mamba feature extraction with an information selection mechanism. The aim is to overcome the shortcomings of traditional methods in complex scenarios while simplifying the training process of deep learning-based fusion strategies. In the image encoding phase， we introduce the Spatial Context Aware Module （SCAM） for the first time in the field of image fusion， which integrates contextual feature information and allocates attention weights adaptively. Then， we design the CNN-Mamba Feature Extract （CMFE） module， which integrates CNN and Mamba networks to perform deep feature extraction on both modalities of the image.In the fusion phase， considering the information differences between infrared and visible images in the same scene， we propose a novel adaptive weight selection fusion layer to retain more source information. Specifically， we calculate the normalised mutual information between the two modalities and set a threshold. When the normalised mutual information exceeds the threshold， the fusion weights are set to be equal to each other. When the mutual information is below the threshold， we assign higher weights to the modality with higher information entropy to preserve more source information. Finally， in the decoding phase， we utilise a decoder consisting of four CNN modules to progressively reduce the number of channels and reconstruct the fused image.

During the experimental phase， ablation studies validate the rationality of the proposed network structure and loss function design. When compared with seven advanced fusion methods， our method demonstrates excellent performance on the MSRS， Road-Scene， TNO， and M3FD public datasets， ranking first in at least three objective metrics. Moreover， the EN and CC values achieve the highest in all tests. This indicates that the proposed method effectively facilitates cross-modal information interaction and preserves the original information as much as possible. Additionally， pedestrian and vehicle detection experiments were conducted on the Road-Scene dataset， and the results show that our method achieved the optimal performance in both pedestrian and vehicle detection， further validating its practical application advantages.

In conclusion， the proposed method demonstrates significant advantages in both image fusion quality and subsequent applications.

In the field of high-resolution optical remote sensing image analysis， target detection and recognition technologies serve as foundational pillars for critical applications such as environmental monitoring， urban planning， agricultural yield estimation， and military reconnaissance. In recent years， deep learning algorithms have revolutionized this domain by leveraging their exceptional ability to extract intricate features： they efficiently capture both the geometric characteristics of ground objects （e.g.， shape contours， spatial distribution patterns， and structural details） and spectral attributes （e.g.， band reflectance variations， spectral curve uniqueness across land covers）. This capability has driven transformative breakthroughs in target detection for high-resolution optical remote sensing images， enabling more accurate and automated identification of diverse ground targets compared to traditional handcrafted-feature-based methods.

However， the practical application of deep learning in multispectral remote sensing image analysis is hindered by inherent challenges stemming from data acquisition and scene complexity. First， multispectral images exhibit significant variability in both image quality and spatial resolution due to differences in sensor platforms—ranging from satellite-borne sensors （with meter to sub-meter resolution） to Unmanned Aerial Vehicle （UAV） cameras and airborne imaging systems. These hardware discrepancies lead to inconsistencies in Signal-to-Noise Ratio （SNR）， radiometric accuracy， and spatial granularity， directly impacting the reliability of feature extraction. Second， remote sensing scenes are inherently complex and variable： factors such as topographic relief， cloud cover， atmospheric scattering， and heterogeneous land cover （e.g.， intermingled vegetation， buildings， and water bodies） create a cluttered background that obscures small targets （e.g.， small vehicles， individual buildings， or vegetation patches）. These challenges are particularly acute for small target detection in heterogeneous scenes， where traditional deep learning models often suffer from high false detection rates， missed detections， or degraded localization accuracy due to insufficient discriminative features or severe background interference.

To address these issues， this study focuses on the SuperYOLO model—a state-of-the-art object detection architecture optimized for remote sensing scenarios—and conducts a systematic investigation to evaluate its performance in small target detection across diverse multispectral datasets. The research specifically analyzes two core factors influencing detection capability： the characteristics of scene datasets and the quality/resolution of multispectral images. For dataset analysis， the study employs two types of experimental data： independent datasets （VEDAI and HRRSD） and a mixed dataset. The VEDAI dataset specializes in small ground targets （primarily vehicles） with consistent data distribution and high annotation precision， while the HRRSD dataset covers multiple small target categories （e.g.， buildings， roads， and small vegetation clusters） in complex urban and natural scenes. In contrast， the mixed dataset is constructed by fusing multispectral data from different sensors， geographic regions， and acquisition times， simulating the high data heterogeneity encountered in real-world remote sensing applications.

The research findings yield three key insights. First， dataset homogeneity significantly impacts SuperYOLO's detection accuracy： the model achieves notably higher performance on independent datasets （VEDAI and HRRSD） compared to the mixed dataset. This is attributed to the consistent data distribution and reduced background variability in independent datasets， which minimize the model's sensitivity to irrelevant background features； in contrast， the mixed dataset's diverse land cover types and spectral variations increase the difficulty of distinguishing small targets from cluttered backgrounds， leading to reduced detection stability. Second， instrument noise emerges as a critical limiting factor for SuperYOLO's precision. When 5% and 10% instrument noise （simulating real-world sensor electronic noise， radiometric distortion， and atmospheric interference） are introduced into multispectral images， the model's mean Average Precision （AP） at an Intersection-over-Union （IoU） threshold of 0.5 （mAP@0.5） plummets sharply from the original 0.813 to 0.420 and 0.058， respectively. This degradation occurs because noise disrupts the integrity of spectral features in multispectral bands， interfering with the multi-branch network architecture of SuperYOLO—specifically impairing its ability to extract and fuse discriminative spectral and geometric features for small targets. Notably， integrating a Denoising Convolutional Neural Network （DCNN） into the data preprocessing module effectively mitigates this issue： the DCNN's adaptive noise-filtering capability restores spectral feature integrity， leading to a significant recovery of SuperYOLO's detection accuracy. Third， the spatial resolution of multispectral images exerts a relatively minor impact on SuperYOLO's performance. After downsampling the original images to simulate lower-resolution scenarios， the model's mAP@0.5 decreases from 0.813 to 0.741 （for mild downsampling） and 0.464 （for severe downsampling）. This resilience is attributed to SuperYOLO's feature pyramid structure， which enables multi-scale feature fusion to compensate for partial resolution loss—though severe downsampling still causes accuracy degradation due to the loss of fine-grained details in small targets.

In conclusion， instrument noise significantly impairs the extraction and fusion of spectral features in SuperYOLO's multi-branch architecture， ultimately limiting its detection accuracy and practical utility for small targets in multispectral remote sensing images. Enhancing optical image quality （e.g.， optimizing sensor hardware to reduce inherent noise） and incorporating denoising algorithms （such as DCNN） into the SuperYOLO framework can substantially improve the detection capability， accuracy， and applicability of small target detection algorithms in multispectral remote sensing imagery. This study emphasizes that maximizing the potential of deep learning models in remote sensing applications requires prioritizing solutions to sensor-induced noise and optimizing image preprocessing strategies. The proposed integration of denoising techniques with advanced detection architectures like SuperYOLO not only provides a more practical technical solution for small target detection in multispectral remote sensing but also points to a promising direction for future research （e.g.， multi-modal data fusion detection， real-time remote sensing image processing） and engineering applications （e.g.， UAV inspection systems， satellite remote sensing monitoring platforms） in this field.

With the wide application of intelligent devices and the development of artificial intelligence technology， significant value has been demonstrated by nighttime image enhancement in multiple fields such as visual Internet of Things， autonomous driving， medical imaging， and object detection. Nighttime image enhancement is defined as the process of increasing the brightness， enhancing the contrast and restoring the details of images under insufficient lighting conditions.To address the issues of texture detail loss and noise amplification in existing nighttime image enhancement methods， a nighttime image enhancement method based on a multi-scale fusion network （MSFNet） is proposed in this paper.

Firstly， in order to effectively extract the texture detail features of the dark area of the image， a dark area feature extraction module is introduced at the front end of the backbone network. The global features of the image are extracted through convolution to generate channel attention weights， enhancing the channel features related to the detail information. Secondly， multi-branch network modules are designed in the backbone network， and specific sub-networks are designed for different scales， so that rich multi-scale information can be perceived during the enhancement process and the loss of detail reconstruction at a single scale is avoided. Finally， a dynamic weight allocation mechanism is proposed in this paper， through which the multi-scale features of each sub-network are adaptively fused to enhance the feature expression ability and further improve the image enhancement effect.

On the three paired datasets of LOLv1， LOLv2-real， and LOLv2-syn， the PSNR values of MSFNet were higher than those of Retinexformer by 1.37， 0.72， and 0.50， respectively. The SSIM values on the LOLv1 and LOLv2-syn datasets were also higher than those of Retinexformer by 0.012 and 0.007， respectively. On the three unpaired datasets of LIME， MEF， and NPE， the optimal NIQE values were achieved by MSFNet， with values as low as 3.74， 3.91， and 3.36， respectively. The brightness of the enhanced images was significantly improved， and the details were rendered more clearly. Furthermore， on the publicly available nighttime face detection dataset DARKFACE， higher face detection accuracy was achieved for the enhanced images. The AP values under the three IoU thresholds of 0.5， 0.6， and 0.7 were recorded as 0.264， 0.107， and 0.014， respectively. More detail information was extracted from the images enhanced by MSFNet， thereby enhancing the discrimination between the target and the background and demonstrating the effectiveness of this method in downstream tasks.

MSFNet is proposed to address the the issues of texture detail loss and noise amplification commonly encountered during enhancement. First， a dark area feature extraction module is introduced at the front end of the backbone； channel attention weights are generated via convolutions on image features， and channel features related to fine details are emphasized， thereby enabling effective extraction of low-light texture details. Second， multi-branch network modules are designed， in which three sub-networks in parallel extract image features at different scales， allowing the recovery of global structure and local details in nighttime images. Finally， a dynamic weight allocation mechanism is proposed， by which multi-scale features from the sub-networks are adaptively fused； the feature representation capability is strengthened， and image enhancement performance is further improved. MSFNet is shown by the experimental results to outperform existing methods on multiple datasets. In comparative experiments on night face detection， a significant improvement in night target detection accuracy is achieved by MSFNet， thereby verifying its practical value in downstream visual tasks.

The accurate measurement of explosion point coordinates is a critical technology for assessing weapon strike efficiency and damage effects in modern warfare. Traditional optical and acoustic methods often suffer from environmental interference， high equipment costs， or a lack of real-time capability. Furthermore， emerging deep learning models require vast amounts of classified training data and high-performance computing resources， which limits their deployment on resource-constrained airborne embedded platforms. To address these challenges， this study proposes an infrared image localization method that integrates ground cooperative markers. The primary objective is to leverage the strong penetration and anti-occlusion advantages of long-wave infrared imaging to achieve high-precision， real-time explosion point detection within a lightweight framework， thereby eliminating the heavy data dependency characteristic of deep learning approaches.

The methodology utilized a pinhole imaging model and a specialized set of ground cooperative markers—including cross， L-shape， straight line， and dot—to establish a stable spatial coordinate system. To facilitate marker identification on an airborne embedded platform， an enhanced convex hull detection algorithm was developed. This algorithm introduced an adjacency constraint to merge points within a 2-pixel threshold， preventing the misidentification of a “cross” caused by imaging fluctuations. Additionally， a maximum distance threshold of 18 pixels was established to distinguish between “cross” and “L-shape” markers when they exhibited similar vertex counts. Characteristic points， such as the centroid of the “cross” and the right-angle vertex of the “L-shape”， were calculated using image moments and geometric side-length comparisons to serve as the origin and axis points for the coordinate system. To maintain stability during Unmanned Aerial Vehicle （UAV） rotation， a coordinate projection transformation model using vector normalization was applied. Finally， a homography matrix was solved using singular value decomposition to map detected explosion point pixels to their actual physical world coordinates.

Experimental validation was conducted through both simulations and practical tests at a height of 13 meters， utilizing an infrared camera mounted on a rotary base to simulate airborne flight dynamics. In a dataset of 1，666 frames capturing various angles and interference conditions， the enhanced convex hull detection algorithm achieved a comprehensive recognition accuracy of 98.2% for the cooperative markers. This performance significantly outperformed that of traditional algorithms， which struggled particularly with the “L-shape” markers， leading to a much lower coordinate system establishment success rate of 46.3%. In simulation-based detection， the enhanced method achieved a mean absolute error of approximately 0.01 meters with a relative error of only 0.14%. Practical experiments using fireworks to simulate explosion points demonstrated that the system could maintain high precision even under the influence of wind fields and thermal fluctuations. The average measured distance for the simulated explosion point was 1 636.6 mm against a reference of 1 631.0 mm， resulting in a measurement accuracy of 0.056 meters and a relative error of 0.3%. These results indicate a substantial improvement in precision over both traditional methods and the YOLOv8 model， the latter of which was constrained by the small target nature of the markers and limited training data.

The research successfully demonstrates that a refined traditional image processing approach can effectively alleviate the data-dependency issues of deep learning in range-based applications. By combining specialized ground markers with an enhanced convex hull detection mechanism， the method enables reliable UAV localization and stable spatial coordinate construction in a simple， embedded-friendly model. The experimental outcomes confirm that the system is robust against camera rotation and background interference， providing a reliable technical foundation for the accurate measurement of explosion positions. While the current resolution of infrared cameras and the assumption of a two-dimensional terrain pose limitations， future research will focus on improving accuracy with higher-resolution sensors and testing the algorithm's stability on a dedicated fixed-wing UAV platform to account for real-world flight jitters and complex terrain undulations.

The rapid proliferation of modern electronic devices has made Electromagnetic Interference （EMI） a crucial challenge， impacting the reliability of optical systems and human health. Transparent optical windows， ubiquitous in aerospace， medical imaging， defense， and communication systems， require materials exhibiting simultaneously high optical transparency， robust EMI shielding， and minimal higher-order diffraction energy. While conventional periodic metal meshes provide effective EMI shielding， their regular structures generate orthogonal array diffraction patterns that compromise the resolution and performance of imaging systems. Alternative transparent EMI shielding materials， including Transparent Conductive Oxides （TCOs）， carbon-based materials， two-dimensional transition metal carbides/nitrides （MXenes）， conductive polymers， and metal nanowires， typically exhibit limitations in simultaneously achieving high optical transparency and robust broadband EMI Shielding Effectiveness （SE）. To overcome these limitations， this study introduces a fabrication technique for structurally randomized metal meshes based on a random crack-template method， integrated with comprehensive theoretical modeling. This strategy optimizes optical and electromagnetic performance while suppressing and homogenizing higher-order diffraction energy.

Fabrication of the structurally randomized metal meshes commenced with the synthesis of an acrylic resin crack-template precursor featuring tunable physical properties. This precursor was spin-coated onto a quartz substrate to generate a random crack template （~6.5 µm depth）. Copper was subsequently deposited into the crack grooves via electron-beam-assisted evaporation. Finally， the crack template was removed using an organic solvent. Scanning Electron Microscopy （SEM） and energy dispersive X-Ray Spectroscopy （EDX） analyses confirmed the formation of a random copper mesh characterized by uniform metal line thickness （~300 nm） and high connectivity. A multiphysics-coupled framework integrating structural parameters， optical performance， and electromagnetic performance was developed to evaluate the random copper mesh. Optical performance was assessed through transmittance （T_{\mathrm{o}\mathrm{p}\mathrm{t}}） and total reflectance （R_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}）， incorporating the effects of metal line coverage， surface roughness （modeled using the Beckmann scattering formulation）， and multipath interference. Diffraction analysis employs Fraunhofer theory to quantify the suppression of higher-order diffraction energy by structural randomization. The electromagnetic shielding model establishes quantitative correlations between geometric parameters， electrical properties （sheet resistance R_{\mathrm{s}}）， and total shielding effectiveness （\mathrm{S}{\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=\mathrm{S}{\mathrm{E}}_{\mathrm{R}}+\mathrm{S}{\mathrm{E}}_{\mathrm{A}}+\mathrm{S}{\mathrm{E}}_{\mathrm{M}}）.

Experimental results demonstrate an average visible-light transmittance （380~760 nm） of 85.5% with a 7.6% loss relative to the quartz substrate， and an average reflectivity of 10.3% is tested at a 6° incidence. Bidirectional Reflectance/Transmittance Distribution Function （BRDF/BTDF） measurements indicated that the random copper mesh optical window exhibited predominantly specular reflection， with scattered light intensity as low as 10^-4 1/sr， demonstrating excellent optical uniformity. The He-Ne laser diffraction testing revealed a uniform diffraction pattern. The fabricated random copper mesh demonstrated favorable electrical conductivity， exhibiting a sheet resistance of 14.38 Ω/sq. This measured value slightly exceeded the theoretical prediction （11.69 Ω/sq）， primarily due to imperfect connectivity within the mesh structure， manifested as isolated metal lines. Furthermore， the random copper mesh achieved an average EMI SE of 24.56 dB across the 1~18 GHz frequency band， as independently verified by Vector Network Analyzer （VNA） measurements and CST Microwave Studio simulations. The inherent structural randomness of the crack-template method effectively suppressed the orthogonal array stray light. Critically， the developed coupled optical-electromagnetic performance model establishes quantitative relationships between structural parameters and functional metrics， facilitating scalable industrial production. This innovation successfully resolves the inherent trade-off between EMI shielding and optical transparency， offering significant promise for precision imaging applications in environments with high EMI. Future investigations will explore advanced materials （such as silver and graphene） and hybrid architectures to extend performance across broader electromagnetic spectral ranges.

With the increasingly serious problem of commodity forgery， it is of great significance to develop new anti-counterfeiting technologies. Photonic crystals have shown broad application prospects in the field of anti-counterfeiting due to their unique optical properties， especially structural color effects. However， traditional photonic crystal materials generally have problems such as poor mechanical properties and insufficient thermal stability， which limit their practical application.

In this study， a flexible composite film of photonic crystals based on epoxy resin （EP） and thermoplastic polyurethane （TPU） interpenetrating network structure was innovatively proposed. Monodisperse silica colloidal microspheres were prepared by the improved Stöber method， and photonic crystal arrays with opal structure were assembled by the vertical deposition method. On this basis， TPU/EP-based photonic crystal structural color films were prepared by a filling and curing process using TPU and EP mixture as flexible substrates. The optical properties and mechanical properties of the composite films were systematically studied by adjusting the ratio of TPU to EP.

The experimental results show that when the mass ratio is 1∶1， the film exhibits excellent structural color rendering and flexibility， with a clear reflectance peak that shifts little after multiple bending. Furthermore， through optical and mechanical tests， it is confirmed that the film exhibits dynamic structural color change at different angles and has excellent durability and environmental stability. By combining the iridescence effect of photonic crystals with fluorescence recognition technology， the film exhibits iridescence under natural light and displays specific fluorescence patterns under ultraviolet light excitation， achieving dual anti-counterfeiting through the combination of “iridescence + fluorescence”.

The practical application tests show that the film can effectively adhere to the surface of the shoes， and the anti-counterfeiting label is difficult to replicate. It can be quickly verified and has good practicability and market potential. It also provides an innovative solution for dynamic anti-counterfeiting certification of footwear products.

Gallium nitride （GaN） is a cornerstone of third-generation wide-bandgap semiconductor technology， valued for its exceptional electronic mobility， thermal stability， chemical inertness， and robustness under extreme operating conditions. These qualities have driven its adoption in high-power electronics， high-frequency devices， and ultraviolet optoelectronics. However， a persistent challenge in GaN-based photonics is achieving efficient and stable red emission. Conventional InGaN-based red Light-Emitting Diodes （LEDs） suffer from efficiency droop and phase separation issues inherent to the high indium （In） content required for red wavelengths. As an alternative， doping GaN with rare-earth （RE） ions—which exhibit sharp， stable intra-4f shell emissions largely independent of the host matrix—offers a promising route to high-color-purity red light. Among these， trivalent europium ions （Eu³⁺） are renowned for their intense red emission lines around 620 nm. To enhance the optical activation and emission efficiency of such RE centers， co-doping with a sensitizer ion is a widely employed strategy. Samarium ions （Sm³⁺） have demonstrated effective sensitization of Eu³⁺ luminescence in various host materials through resonant energy transfer. Nevertheless， the specific system of Sm³⁺/Eu³⁺ co-doped GaN is still in its infancy. This study aims to systematically investigate the structural modifications and luminescence characteristics—particularly the energy transfer dynamics—in GaN thin films co-doped with Sm³⁺ and Eu³⁺ ions， thereby evaluating its potential as a novel red-emitting material platform compatible with established GaN semiconductor processes.

This research employed ion implantation as the doping technique to introduce Sm³⁺ and Eu³⁺ ions into high-quality GaN thin films grown on c-plane sapphire substrates via metal-organic chemical vapor deposition （MOCVD）. A series of samples were prepared： including Sm³⁺ single-doped samples with varying doses （1×10¹⁴， 5×10¹⁴， 1×10¹⁵ at/cm²）， and Sm³⁺/Eu³⁺ co-doped samples with a fixed Sm³⁺ dose （5×10¹⁴ at/cm²） and varying Eu doses （1×10¹⁴， 5×10¹⁴， 1×10¹⁵ at/cm²）. Post-implantation thermal annealing at 1 000 ℃ in an ammonia atmosphere was conducted to recover implantation-induced lattice damage and to optically activate the RE ions. The structural integrity and stress state of the doped films were comprehensively assessed using High-resolution X-ray Diffraction （HRXRD） and Raman spectroscopy. The optical emission properties were investigated using Cathodoluminescence （CL） spectroscopy.

HRXRD rocking curve analysis of the GaN （0002） plane revealed that the Full Width at Half Maximum （FWHM） increased with higher Sm³⁺ concentrations in single-doped samples， indicating a degradation of crystalline quality due to implantation-induced defects. In co-doped samples with a fixed Sm³⁺ dose， the FWHM exhibited a complex， non-monotonic trend as the Eu³⁺ dose increased， showing a slight initial decrease followed by a rise at higher doses. This suggests that the incorporation of a low dose of Eu³⁺ may induce minor lattice distortions that slightly improve local ordering， while higher doses exacerbate defect formation. Lattice constant analysis from 2θ/ω scans and Raman shift measurements of the E₂^high phonon mode provided consistent evidence of stress evolution. Sm³⁺ single-doping introduced tensile stress， whereas the introduction of Eu³⁺ in co-doped samples led to a significant increase in compressive stress， which intensified with higher Eu³⁺ doses.

Cathodoluminescence spectroscopy provided clear evidence of efficient energy transfer from Sm³⁺ （sensitizer） to Eu³⁺ （activator）. In Sm³⁺ single-doped samples， characteristic emission peaks were observed from Sm³⁺ at approximately 577 nm （⁴G_5/2 → ⁶H_5/2）， 618 nm （⁴G_5/2 → ⁶H_7/2）， and 666 nm （⁴G_5/2 → ⁶H_7/2）. In the co-doped samples， the Sm³⁺ emission intensity， particularly the 577 nm peak， was significantly quenched， while the characteristic Eu³⁺ emission lines， especially the dominant red emission at ~620 nm （⁵D₀ → ⁷F₂）， were strongly enhanced with increasing Eu³⁺ concentration. This anti-correlation provides clear evidence for a non-radiative energy transfer process. The primary energy transfer pathway was identified as ⁴G_5/2 （Sm³⁺） + ⁷F₀ （Eu³⁺） → ⁶H_5/2 （Sm³⁺） + ⁵D₀ （Eu³⁺）， favored by the near-perfect resonance （energy mismatch of only ~74 meV） between the Sm³⁺⁴G_5/2 → ⁶H_5/2 transition and the Eu³⁺⁷F₀→⁵D₀ absorption.

Further quantitative analysis based on Dexter's theory was conducted to elucidate the dominant interaction mechanism. By analyzing the dependence of the Sm³⁺ emission quenching ratio on the total RE concentration through fitting， it was determined that energy transfer occurs primarily via electric dipole-dipole and electric dipole-quadrupole interactions. Critical energy transfer distances （R_c） were calculated to lie between 1.66 nm and 2.24 nm for the studied concentration range， confirming the feasibility of efficient resonant transfer at these doping levels.

The impact of co-doping on the emitted light color was evaluated using the Commission Internationale de l'Éclairage （CIE） chromaticity coordinates. As the Eu³⁺ dose increased from 1×10¹⁴ to 1×10¹⁵ at/cm²， the CIE coordinates systematically shifted from （0.527 5， 0.369 5） in the yellowish-red region to （0.606 3， 0.353 2） in the deep red region. Correspondingly， the calculated Correlated Color Temperature （CCT） decreased from 1 580 K to 730 K. This demonstrates that by adjusting the concentration of Eu³⁺ relative to the fixed proportion of the Sm³⁺ sensitizer， a clear and controllable tuning of the emission color toward saturated red can be achieved.

In conclusion， this study successfully fabricated and characterized Sm³⁺/Eu³⁺ co-doped GaN thin films via ion implantation. Although the doping process introduces compressive stress and affects the crystalline quality， it enables efficient resonant energy transfer from Sm³⁺ to Eu³⁺. This transfer effectively pumps the Eu³⁺⁵D₀ level， resulting in intensified red emission around 620 nm. Most notably， the emission color can be tuned from yellowish-red to deep red by varying the Eu³⁺ concentration， providing a direct means to achieve targeted red chromaticity. These findings offer foundational experimental insights and a material processing framework for developing GaN-based red-emitting devices utilizing the Sm³⁺/Eu³⁺ co-doping scheme， presenting a potentially advantageous alternative to high-In-content InGaN for red micro-LEDs and other advanced optoelectronic applications. Future work may focus on optimizing implantation and annealing parameters to mitigate structural damage and further enhance the luminescence efficiency of this co-doped system.