Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional ...Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at https://github.com/CHEN-hJ516/RetinexWT(accessed on 14 October 2025).展开更多
随着智能矿井建设不断推进,图像在矿山安全监测、设备识别与作业辅助中发挥着重要作用。然而,矿井图像常面临低照度、光照不均、噪声干扰等复杂环境问题,导致图像细节模糊、亮度失衡,严重影响后续图像识别与智能分析的准确性。为解决上...随着智能矿井建设不断推进,图像在矿山安全监测、设备识别与作业辅助中发挥着重要作用。然而,矿井图像常面临低照度、光照不均、噪声干扰等复杂环境问题,导致图像细节模糊、亮度失衡,严重影响后续图像识别与智能分析的准确性。为解决上述问题,提出了一种融合多尺度增强机制与色调、饱和度和亮度色彩空间的矿井图像增强算法。该算法以Retinex理论构建的深度增强网络为基础,首先将矿井图像分解为光照与反射2个成分。针对光照成分,设计多尺度卷积网络提取不同空间尺度下的亮度信息,增强全局光照建模能力;针对反射成分,引入双边滤波机制进行噪声抑制与边缘结构保留。然后,分别将优化后的光照与反射成分通过融合重构形成初步增强图像。最后,在HSV色彩空间中分离初步增强图像的亮度通道,引入曝光调整与细节增强模块,进一步实现亮度补偿与纹理还原的联合优化。试验结果表明,所提方法在DIV2K公开数据集中的峰值信噪比高达28.9 d B,结构相似性指数达到0.87。在自制的矿井图像数据集上,该算法的特征相似度指数最高提升至0.902,通用图像质量指数最高达0.847。在不同光照条件下,该方法均表现出良好的细节恢复与亮度均衡能力,验证了其在矿井图像增强中的有效性。展开更多
基金supported in part by the National Natural Science Foundation of China[Grant number 62471075]the Major Science and Technology Project Grant of the Chongqing Municipal Education Commission[Grant number KJZD-M202301901].
文摘Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at https://github.com/CHEN-hJ516/RetinexWT(accessed on 14 October 2025).
文摘随着智能矿井建设不断推进,图像在矿山安全监测、设备识别与作业辅助中发挥着重要作用。然而,矿井图像常面临低照度、光照不均、噪声干扰等复杂环境问题,导致图像细节模糊、亮度失衡,严重影响后续图像识别与智能分析的准确性。为解决上述问题,提出了一种融合多尺度增强机制与色调、饱和度和亮度色彩空间的矿井图像增强算法。该算法以Retinex理论构建的深度增强网络为基础,首先将矿井图像分解为光照与反射2个成分。针对光照成分,设计多尺度卷积网络提取不同空间尺度下的亮度信息,增强全局光照建模能力;针对反射成分,引入双边滤波机制进行噪声抑制与边缘结构保留。然后,分别将优化后的光照与反射成分通过融合重构形成初步增强图像。最后,在HSV色彩空间中分离初步增强图像的亮度通道,引入曝光调整与细节增强模块,进一步实现亮度补偿与纹理还原的联合优化。试验结果表明,所提方法在DIV2K公开数据集中的峰值信噪比高达28.9 d B,结构相似性指数达到0.87。在自制的矿井图像数据集上,该算法的特征相似度指数最高提升至0.902,通用图像质量指数最高达0.847。在不同光照条件下,该方法均表现出良好的细节恢复与亮度均衡能力,验证了其在矿井图像增强中的有效性。
