Structured light 3D reconstruction is widely used in high-precision metrology and industrial inspection because of its non-contact and high-resolution characteristics. In practical industrial environments, however, captured fringe patterns are often corrupted by composite noise originating from sensor electronics, surface reflectance variations, and ambient interference. Such noise degrades phase quality, introduces unwrapping errors, and limits the achievable reconstruction accuracy. To address these issues, this paper proposes a multi-modal deep denoising method that integrates a dual-channel input, a squeeze-and-excitation channel attention mechanism, and a hybrid loss function combining MSE, SSIM, and Laplacian edge constraints. The objective is to effectively suppress multi-source noise while preserving fringe continuity and edge structures, thereby enhancing phase retrieval stability, reducing unwrapping failures, and ultimately improving both image-domain quality and 3D reconstruction accuracy under complex industrial conditions.

The proposed method employs a dual-channel residual convolutional neural network based on the DnCNN architecture. The input is constructed by concatenating the original noisy fringe pattern with a serially filtered image along the channel dimension. The serial filtering module first applies extreme value detection and selective median filtering to remove impulse noise, followed by two-dimensional Gaussian smoothing to suppress high-frequency random noise. Multi-scale convolutional layers with batch normalization and ReLU activation are then introduced to extract hierarchical fringe features from the dual-channel input, capturing both fine textures and coarse structures while preserving edge information.

Subsequently, squeeze-and-excitation (SE) channel attention modules are embedded after convolutional layers in each residual block to adaptively emphasize informative features. Each SE module performs global average pooling to compress spatial information, followed by a bottleneck mapping with ReLU and Sigmoid activation to generate channel-wise weights, which are then used to recalibrate the feature maps. A hybrid loss function is designed to balance global noise suppression and local structure preservation, combining mean squared error for luminance fidelity, structural similarity index for contrast and texture consistency, and a Laplacian-based edge loss to maintain high-frequency details and boundary sharpness.

Experiments are conducted on a binocular structured light system using three-frequency four-step phase shifting and heterodyne phase unwrapping. The left camera coordinate system is defined as the world coordinate system, and 3D coordinates are obtained via linear triangulation with calibrated camera and projector parameters. Training and validation data are collected from three representative objects: standard step blocks (rule geometries with sharp edges), plaster busts (smooth curved surfaces), and printed circuit boards (high-reflectivity components and fine textures). Reference images are obtained through multi-frame averaging.

Quantitative evaluations in the image domain show that the proposed method achieves a PSNR of 43.11 dB / SSIM of 0.9654 on the standard step block, 42.90 dB / 0.9627 on the plaster bust, and 42.69 dB / 0.9643 on the PCB. Compared with noisy fringe patterns, the average PSNR increases by approximately 7 dB, SSIM improves to about 0.96, and RMSE decreases by around 61.6%.Ablation studies confirm the contribution of each component: dual-channel input improves PSNR by approximately 1.05 dB over single-channel input; the SE attention module provides consistent gains in both PSNR and SSIM, especially in edge detail recovery; and the hybrid loss function achieves the best overall performance. Comparative experiments against traditional methods (NLM, BM3D) and learning-based methods (BM3D-Net, DIVA) demonstrate that the proposed method achieves the highest PSNR (42.90 dB), highest SSIM (0.9627), and lowest RMSE (0.0063) on the plaster bust dataset. Noise robustness analysis under varying Gaussian noise levels (σ_g = 5 to 20) shows that even at the strongest noise level (σ_g = 20), the proposed method maintains a PSNR of 27.6 dB and an SSIM of 0.8032, substantially outperforming the original DnCNN (25.6 dB, 0.7514) and noisy inputs (15.7 dB, 0.4057), thereby demonstrating a high noise tolerance.

In the phase domain, denoised fringe patterns produce wrapped and absolute phase maps with significantly fewer phase jumps and fringe discontinuities, particularly in edge regions and high-reflectivity areas. Phase gradient analysis shows that the denoised histogram is more concentrated near zero, with a marked reduction in high-gradient outliers. In the 3D reconstruction domain, the point-to-plane RMSE decreases by 47.1%, the 95% quantile error decreases by 42.4%, the maximum error decreases by 16.7%, and point cloud density increases by 43.6%. Connectivity analysis reveals that high-error regions transition from large clustered patches to sparse isolated spots, indicating effective error tail suppression and spatial homogenization without introducing new artifacts. Region-wise error analysis further shows that RMSE and 95% quantile error decrease in both flat and edge regions, while edge metrics such as boundary accuracy, boundary completeness, and normal angle errors all improve. Consequently, the proposed method preserves key structural and edge details while suppressing noise, confirming that no edge rounding or structural weakening occurs.

This paper proposes a deep denoising method for structured light fringe patterns that integrates multi-modal input and structure-preserving mechanisms. Based on an improved DnCNN framework, the method employs a dual-channel input combining the original noisy fringe pattern with a serially filtered image, introduces a squeeze-and-excitation channel attention mechanism, and adopts a hybrid loss function. Experiments on standard step blocks, plaster busts, and printed circuit boards demonstrate significant improvements in PSNR, SSIM, and RMSE, while maintaining texture preservation and fringe continuity. In the 3D reconstruction domain, point cloud density, accuracy, and structural fidelity are substantially enhanced. The proposed method provides a reliable preprocessing strategy for high-precision industrial measurement and defect detection.

Infrared target detection plays an indispensable role in numerous critical domains, such as security surveillance, autonomous driving, and military reconnaissance, owing to its unique perceptual capability under complex environments (e.g., low-light conditions and severe weather). However, infrared images inherently suffer from low contrast, blurred details, and significant noise interference, which often lead to ambiguous target edges, missing texture features, and other challenges during the detection process. Existing deep learning-based infrared target detection algorithms (ITDA) exhibit inadequate performance in feature extraction and processing for infrared images, resulting in relatively high rates of missed detection and false detection. Moreover, our systematic analysis of infrared target detection tasks reveals that algorithms tailored for small infrared targets rely heavily on high-sensitivity feature extraction to capture subtle characteristics. Nevertheless, as the scale of detected targets increases, these algorithms tend to encounter overfitting to local textures and elevated false detection rates, thereby degrading overall performance. In practical applications, detection environments are dynamically changing with targets of varying scales; thus, multi-scale detection capability is critical to ensuring algorithms maintain high reliability and adaptability in complex real-world scenarios. Unfortunately, most state-of-the-art algorithms are optimized for single-scale targets, making it challenging to simultaneously satisfy the requirements of high-precision localization for small targets and effective semantic understanding for large targets.

To address the above issues, this paper proposes an adaptive multi-scale infrared target detection network based on YOLO (AFITDYOLO). This network is designed to receive infrared target images of different scales and employs a multi-layer feature extraction module and a multi-layer feature fusion module to enhance its multi-scale infrared target detection capability. Firstly, a multi-scale feature fusion module (MFFM) is proposed. This module enhances the correlation between features of different layers in the feature pyramid network (FPN), coordinates deep semantic features with shallow spatial detail features more effectively, and thereby improves the representational ability of multi-scale feature fusion. Secondly, a multi-kernel feature extraction convolution (MFEConv) is constructed. By utilizing heterogeneous convolution groups, MFEConv expands the receptive field and strengthens the model's feature extraction capability. Additionally, a cross-attention fusion module (CAFM) is designed. Through the comparative interaction of feature maps output by different layers in the detection network, CAFM leverages the complementary information among these feature maps to suppress infrared noise in images and further enhance feature representation capability.

To validate the effectiveness of the proposed method in improving detection performance, extensive training and evaluation are conducted on the CTIR dataset, which comprises road pedestrians and vehicles with multi-scale infrared targets. To further verify the adaptability of the method, additional experiments are performed on the SIRST-UAVB dataset—a single-frame UAV bird dataset characterized by more complex backgrounds and smaller target scales. Experimental results on these two datasets demonstrate that AFITDYOLO achieves mean average precision at 50% intersection over union (mAP50) of 88.9% and 90.7%, respectively, representing significant improvements of 5.6% and 6.5% compared with YOLOv10n. In terms of lightweight optimization, the proposed method achieves higher inference speed (measured in frames per second, FPS) while utilizing fewer model parameters (params) and floating-point operations (FLOPs). When compared with current mainstream methods, AFITDYOLO exhibits the highest detection accuracy, the lowest parameter count and FLOPs, and the fastest inference speed, demonstrating distinct advantages. Additionally, to evaluate the generalization ability of the proposed method, cross-dataset experiments are carried out on the HIT-UAV dataset (a high-altitude UAV infrared thermal imaging dataset) and the IRSTD-1k dataset (a classic infrared small target dataset). Experimental results indicate that while the precision (P) value of AFITDYOLO is slightly inferior to that of DEIM-N, it outperforms all other mainstream methods in all remaining evaluation metrics. These findings confirm that the proposed method achieves improved detection accuracy on the infrared datasets used in the generalization experiments, validating its strong generalization capability and further demonstrating its feasibility for cross-scenario deployment. Overall, the proposed method simultaneously achieves enhanced detection accuracy and lightweight optimization of the detection model, fully meeting the requirements of real-time detection applications.

The AFITDYOLO network proposed in this paper, which is an adaptive multi-scale infrared target detection network based on YOLO, enhances the detection accuracy of infrared targets of different scales under various backgrounds with a relatively small number of parameters. The proposed MFFM enhances the model's representational ability in multi-scale feature fusion by improving the correlation between features of different layers in FPN. Additionally, the lightweight convolution module MFEConv is designed to achieve an efficient and larger receptive field with minimal parameters by leveraging the target distribution characteristics of infrared images. Furthermore, the CAFM is introduced to highlight important feature information, filter out irrelevant background information, and suppress noise through the comparative interaction of feature maps output by different layers, thereby further boosting the model's feature representation capability. Experimental results demonstrate that the proposed method outperforms current mainstream algorithms, exhibiting excellent detection accuracy, lightweight performance, and generalization ability, along with cross-scenario deployment capabilities.