Cheng Zhesheng, Wu Yingdan, Tian Fang, Feng Zaiwen, Li Yan
School of Science, Hubei University of Technology, Wuhan 430068, China.
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Sensors (Basel). 2025 Aug 4;25(15):4789. doi: 10.3390/s25154789.
To address the issues of loss of important detailed features, insufficient contrast enhancement, and high computational complexity in existing low-light image enhancing methodologies, this paper presents a low-light image enhancement network (MSF-ACA), which uses multi-scale feature fusion and adaptive contrast adjustment. Focus is placed on designing the local-global image feature fusion module (LG-IFFB) and the adaptive image contrast enhancement module (AICEB), in which the LG-IFFB adopts the local-global dual-branching structure to extract multi-scale image features, and utilizes the element-by-element multiplication method to fuse the local details with the global illumination distribution to alleviate the problem of serious loss of image details, while the AICEB incorporates linear contrast enhancement and confidence adaptive stopping mechanism, which dynamically adjusts the computational depth according to the confidence of the feature map, balancing the contrast enhancement and computational efficiency. According to the results of the experiment, the parameter count of MSF-ACA is 0.02 M, and compared with today's mainstream algorithms, the suggested model attains 21.53 dB in PSNR when evaluated on the LOL-v2-real evaluation dataset, and the BRI is as low as 16.04 on the unpaired dataset DICM, which provides a better detail clarity and color fidelity in visual enhancement, and it is a highly efficient and robust low-light image model.
为了解决现有低光图像增强方法中重要细节特征丢失、对比度增强不足和计算复杂度高的问题，本文提出了一种低光图像增强网络（MSF-ACA），该网络采用多尺度特征融合和自适应对比度调整。重点设计了局部-全局图像特征融合模块（LG-IFFB）和自适应图像对比度增强模块（AICEB），其中LG-IFFB采用局部-全局双分支结构提取多尺度图像特征，并利用逐元素乘法方法将局部细节与全局光照分布融合，以缓解图像细节严重丢失的问题，而AICEB结合了线性对比度增强和置信度自适应停止机制，根据特征图的置信度动态调整计算深度，平衡对比度增强和计算效率。根据实验结果，MSF-ACA的参数数量为0.02M，与当今主流算法相比，该模型在LOL-v2-real评估数据集上评估时，PSNR达到21.53dB，在未配对数据集DICM上的BRI低至16.04，在视觉增强中提供了更好的细节清晰度和色彩保真度，是一个高效且鲁棒的低光图像模型。
