Low-light image enhancement network based on central difference convolution

Since the convolutional neural networks and transformers used in existing low-light image enhancement methods were prone to ignore high-frequency information, resulting in blurred details of the enhanced image, this affected the performance of computer vision tasks at night. Therefore, we propose a novel low-light image enhancement network based on central difference convolution (CDCLNet). This method uses traditional image processing methods to help the network extract high-frequency information. Specifically, firstly, in order to fully expose the hidden high-frequency details, the proposed method uses the multi-exposure strategy based on bright and dark masks to expose the image to different levels. Secondly, the complementary information between multi-exposure images is fused through the first-stage network. Finally, the second-stage network suppresses the amplified noise and enhances the details. In addition, We design a central difference convolution module (CDCM) with channel attention to adaptively extract gradient-level detailed features according to the need of the two-stage network. In order to make the network notice illumination non-uniformity, we propose a multi-scale feature attention module (MFAM), which extracts multi-scale features in each channel and generates channel-specific attention maps. Experiments on four public datasets show that the proposed method can enhance the details more effectively than mainstream methods, and achieves the highest structural similarity index on two paired datasets, with an average value of 0.899.