Low-light environments in practical computer vision applications，such as security surveillance，autonomous driving，and intelligent urban monitoring，considerably degrade image quality and thus restrict the performance of high-level visual tasks. Images captured under poor illumination often exhibit insufficient brightness，heavy noise amplification，and severe loss of structural and textural information. These degradations not only harm perceptual quality but also compromise the reliability of downstream applications such as object tracking，object detection，and semantic segmentation. Existing methods have attempted to solve these problems by increasing image brightness，but they frequently suffer from inadequate overexposure suppression and heavy reliance on paired datasets collected under normal lighting conditions. Given that such datasets are expensive and difficult to obtain，the generalization ability of these supervised methods remains limited in real-world complex scenarios. Furthermore，some unsupervised methods attempt to reduce dependency on paired data，but they often fail to achieve noise suppression，exposure correction，and structure preservation simultaneously，leading to unsatisfactory performance in practice.

To address these challenges，this study proposes a novel zero-supervision low-light image enhancement framework that utilizes illumination components to guide reflectance estimation，thereby ensuring high-fidelity reconstruction. The framework is designed to operate without paired training data while simultaneously achieving effective noise suppression，overexposure control，and structure preservation. The process begins with a reversible brightness perturbation strategy combined with pseudoexposure generation，which synthesizes abundant pseudo low-light and pseudo well-lit samples without changing the intrinsic reflectance of the image and thus effectively simulates diverse illumination conditions and eliminates the dependence on paired supervision. A dual-domain collaborative attention denoising module is introduced to improve robustness further. This module operates in the spatial and channel domains： in the spatial domain，contextual dependencies are captured to distinguish true structures from random noise，while in the channel domain，correlations among different feature channels are modeled to suppress redundant information and emphasize critical features. By collaborating across these two domains，the module ensures effective denoising while preserving structural fidelity. For structural representation，multilayer residual convolutions and a structurally symmetric U-Net architecture work in concert to generate the illumination map. The symmetric encoder-decoder structure enables extraction of multiscale features，capturing global illumination and local detail，while the residual connections mitigate vanishing gradients and accelerate convergence. The U-Net is designed not only to generate illumination maps but also to extract illumination-related features that serve as explicit guidance for reflectance recovery. On this basis，a guided reflectance estimation method is introduced. Instead of directly treating reflectance as a residual，illumination priors are fused into the estimation process. This fusion enables the network to separate illumination variations effectively from intrinsic structures，restoring edges and textures with high fidelity while suppressing overexposed regions. The integration of illumination priors makes the restored images appear natural and structurally consistent，thereby addressing the shortcomings of previous enhancement methods.

Extensive comparative experiments were conducted on the LOL，DICM，and WildNight-8K datasets against nine representative state-of-the-art low-light image enhancement methods. On the LOL dataset，the proposed method outperformed Retinexformer and LightenDiffusion，achieving reductions of 0.62 and 0.92 in the natural image quality evaluator （NIQE） and decreases of 1.23 and 0.25 in the perceptual index （PI），respectively. On the DICM dataset，compared with Retinexformer and retinex-inspired unrolling with architecture search（RUAS），the proposed approach reduced NIQE by 0.11 and 0.81，and PI by 0.48 and 0.72，respectively. On the WildNight-8K dataset，relative to Retinexformer and EnlightenGAN，NIQE was further reduced by 1.32 and 1.87，while PI decreased by 0.33 and 5.98，respectively. These results demonstrate that the proposed method consistently delivers enhanced images with improved naturalness and perceptual quality across diverse low-light scenarios. In addition to image quality assessment，the enhanced images were further evaluated in a downstream animal detection task using YOLOv8. Experimental results show that the mean average precision （mAP@50–95） increased by 4.60% compared with the original low-light images，indicating that the proposed enhancement method not only improves visual quality but also remarkably boosts the reliability of downstream perception tasks in challenging low-light conditions.

This study introduces a zero-supervision low-light image enhancement framework that integrates illumination-guided reflectance estimation for high-fidelity image reconstruction. The key innovations include reversible brightness perturbation with pseudoexposure generation to remove paired dataset dependency，a dual-domain collaborative attention denoising module to reduce noise while preserving structural details，a residual U-Net backbone for effective multiscale representation，and an illumination-fused reflectance estimation mechanism to achieve structure-preserving restoration and overexposure suppression. Experimental results across multiple datasets and real-world applications demonstrate that the proposed method not only achieves state-of-the-art performance in quantitative and qualitative metrics but also remarkably improves downstream vision tasks，particularly object detection. These contributions indicate strong practical value and broad application potential. Looking forward，future work could explore extending this framework to dynamic video enhancement with temporal consistency，incorporating adaptive illumination priors derived from scene semantics，and optimizing lightweight deployment for edge devices such as autonomous vehicles and mobile cameras. With these directions，the proposed method could further strengthen its role as a reliable solution for low-light visual perception in safety-critical and real-time applications.