Low-Light Image Enhancement via Dual Information-Based Networks

Recently, deep-learning-based low-light image enhancement (LLIE) methods have made great progress. Benefiting from elaborately designed model architectures, these methods enjoy considerable performance gains. However, the generalizability of these methods may be weak, and they may suffer from the overfitting risk in the case of insufficient data as a result. At the same time, their complex model design brings serious computational burdens. To further improve performance, we exploit dual information, including spatial and channel (contextual) information, in the high-dimensional feature space. Specifically, we introduce customized spatial and channel blocks according to the feature difference of different layers. In shallow layers, the feature resolution is close to that of the original input image, and the spatial information is well preserved. Therefore, the spatial restoration block is designed for leveraging such precise spatial information to achieve better spatial restoration, e.g., revealing the textures and suppressing the noise in the dark. In deep layers, the features contain abundant contextual information, which is distributed in various channels. Hence, the channel interaction block is incorporated for better feature interaction, resulting in stronger model representation capability. Combining the U-Net-like model with the customized spatial and channel blocks makes up our method, which effectively utilizes dual information for image enhancement. Through extensive experiments, we demonstrate that our method, despite its simplicity of design, can provide advanced or competitive performance compared to some state-of-the-art deep learning- based methods.