A Multi-Scale Fusion Residual Encoder-Decoder Approach for Low Illumination Image Enhancement

Abstract

In a low-light environment, due to the extremely small number of photons and high noise, the sensory light source of the line-scan camera cannot be fully exposed, resulting in a decrease in image quality. To this end, a low illumination image enhancement approach based on multi-scale fusion residual encoder- decoder is proposed, which directly learn the end-to-end mapping between the original sensor RAW light and dark images, and effectively enhance the brightness of the image while completely restoring the original image details and colors. In order to increase the feature diversity and speed up the network training, the network structure is added with residual block. To aggregate the global multi-scale features of the context, a dense context feature aggregation module is designed to make up for the lack of spatial information in the network. Based on the SID dataset, a comparative experiment with other 10 methods shows that proposed method is significantly better than most other methods in visual effects and quantitative evaluation of PSNR and SSIM. While restoring the brightness of the image, the edges and colors of the image are effectively represented, and finally a satisfactory visual quality is obtained under low light enhancement.