Abstract
In low-light environments, image acquisition devices do not obtain sufficient light sources, resulting in low brightness and contrast of images, which poses a great obstacle for other computer vision tasks to be performed. To enable other vision tasks to be performed smoothly, it is essential to enhance the research on low-light image enhancement algorithms. In this article, a multi-scale feature fusion image enhancement network based on recursive structure is proposed. The network uses a dual attention module-Convolutional Block Attention Module. It was abbreviated as CBAM, which includes two attention mechanisms: channel attention and spatial attention. To extract and fuse multi-scale features, we extend the U-Net model using the inception model to form the Multi-scale inception U-Net Module or MIU module for short. The learning of the whole network is divided into T recursive stages, and the input of each stage is the original low-light image and the inter-mediate estimation result of the output of the previous recursion. In the t-th recursion, CBAM is first used to extract channel feature information and spatial feature information to make the network focus more on the low-light region of the image. Next, the MIU module fuses features from three different scales to obtain inter-mediate enhanced image results. Finally, the inter-mediate enhanced image is stitched with the original input image and fed into the t + 1th recursive iteration. The inter-mediate enhancement result provides higher-order feature information, and the original input image provides lower-order feature information. The entire network outputs the enhanced image after several recursive cycles. We conduct experiments on several public datasets and analyze the experimental results subjectively and objectively. The experimental results show that although the structure of the network in this article is simple, the method in this article can recover the details and increase the brightness of the image better and reduce the image degradation compared with other methods.