Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a non-trivial task. Some endeavors have been recently made to enhance low-light images using convolutional neural networks (CNNs). However, they have low efficiency in learning the structural information and diverse illumination levels at the local regions of an image. Consequently, the enhanced results are affected by unexpected artifacts, such as unbalanced exposure, blur, and color bias. To this end, this paper proposes a novel framework, called ClassLIE, that combines the potential of CNNs and transformers. It classifies and adaptively learns the structural and illumination information from the low-light images in a holistic and regional manner, thus showing better enhancement performance. Our framework first employs a structure and illumination classification (SIC) module to learn the degradation information adaptively. In SIC, we decompose an input image into an illumination map and a reflectance map. A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error on the illumination map. As such, each input image can be divided into patches with three enhancement difficulty levels. Then, a feature learning and fusion (FLF) module is proposed to adaptively learn the feature information with CNNs for different enhancement difficulty levels while learning the long-range dependencies for the patches in a holistic manner. Experiments on five benchmark datasets consistently show our ClassLIE achieves new state-of-the-art performance, with 25.74 PSNR and 0.92 SSIM on the LOL dataset.

翻译：摘要：低光照图像常因可见度受限及多种退化类型而质量不佳，使得低光照图像增强（LIE）成为一项具有挑战性的任务。近期，一些研究尝试利用卷积神经网络（CNN）来增强低光照图像，但这些方法在学习图像局部区域的结构信息和多样化光照水平方面效率较低，导致增强结果出现非期望的伪影，如曝光不均、模糊和色彩偏差。为此，本文提出一种融合CNN与Transformer潜力的新型框架ClassLIE，该方法通过全局与局部相结合的方式对低光照图像中的结构与光照信息进行分类和自适应学习，从而获得更优的增强性能。该框架首先采用结构与光照分类（SIC）模块自适应学习退化信息。在SIC中，我们将输入图像分解为光照图和反射图，进而设计类别预测模块，通过计算反射图的结构相似度得分和光照图的均方误差对退化信息进行分类，从而将每张输入图像划分为三种增强难度的图像块。随后，提出特征学习与融合（FLF）模块，利用CNN对不同难度级别的图像块进行自适应特征学习，同时以全局方式学习图像块间的长程依赖关系。在五个基准数据集上的实验一致表明，ClassLIE取得了新的最优性能，在LOL数据集上PSNR达到25.74，SSIM达到0.92。