Learning to Concurrently Brighten and Mitigate Deterioration in Low-Light Images

Low-light images are susceptible to poor visibility, low contrast, and additive noise. Although various techniques can enhance brightness to a reasonable extent, they inevitably amplify noise and cause color distortion, halo artifacts, and other forms of deterioration. Most low-light image enhancement (LLIE) methods utilize multiple algorithms or models to remove the corresponding deterioration types while brightening the image. However, an optimization strategy can be difficult, primarily because each sub-model has its own set of hyperparameters that must be tuned. To address this problem, we propose an end-to-end network, called CBMD-Net, which concurrently brightens and mitigates the deterioration in low-light images. Specifically, our network is developed on a U-shaped backbone with several stacked pyramid depthwise separable convolution (PDC) modules to accurately extract meaningful spatial features from the network. Additionally, a new upsampling module is presented to efficiently restore the spatial resolution of the feature maps. Finally, we discovered that the latent representations at the network bottleneck contain relevant information regarding the input image, including deteriorated features that can have repercussions on image quality. Therefore, we introduce an intermediate loss function at the bottleneck to mitigate the additive noise and undesirable artifacts. Comprehensive experiments demonstrate that the developed network can obtain satisfactory results from both subjective and objective comparisons against state-of-the-art methods.