Li Rui-Kang, Li Meng-Hao, Chen Shi-Qi, Chen Yue-Ting, Xu Zhi-Hai
Opt Express. 2023 Dec 18;31(26):42887-42900. doi: 10.1364/OE.507966.
Due to severe noise and extremely low illuminance, restoring from low-light images to normal-light images remains challenging. Unpredictable noise can tangle the weak signals, making it difficult for models to learn signals from low-light images, while simply restoring the illumination can lead to noise amplification. To address this dilemma, we propose a multi-stage model that can progressively restore normal-light images from low-light images, namely Dark2Light. Within each stage, We divide the low-light image enhancement (LLIE) into two main problems: (1) illumination enhancement and (2) noise removal. Firstly, we convert the image space from sRGB to linear RGB to ensure that illumination enhancement is approximately linear, and design a contextual transformer block to conduct illumination enhancement in a coarse-to-fine manner. Secondly, a U-Net shaped denoising block is adopted for noise removal. Lastly, we design a dual-supervised attention block to facilitate progressive restoration and feature transfer. Extensive experimental results demonstrate that the proposed Dark2Light outperforms the state-of-the-art LLIE methods both quantitatively and qualitatively.
由于严重的噪声和极低的照度，从低光照图像恢复到正常光照图像仍然具有挑战性。不可预测的噪声会使微弱信号变得混乱，导致模型难以从低光照图像中学习信号，而仅仅恢复照度会导致噪声放大。为了解决这一困境，我们提出了一种多阶段模型，即Dark2Light，它可以逐步从低光照图像恢复到正常光照图像。在每个阶段，我们将低光照图像增强（LLIE）分为两个主要问题：（1）照度增强和（2）噪声去除。首先，我们将图像空间从sRGB转换为线性RGB，以确保照度增强近似线性，并设计一个上下文Transformer模块以粗到细的方式进行照度增强。其次，采用U-Net形状的去噪模块进行噪声去除。最后，我们设计了一个双监督注意力模块，以促进渐进式恢复和特征转移。大量实验结果表明，所提出的Dark2Light在定量和定性方面均优于当前最先进的LLIE方法。
