%0 Journal Article %A GU Feng-Zhu %A PAN Xiao-Yang %A WEI Miao %A YU Hao %T Multi-scale fusion residual encoder-decoder approach for low illumination image enhancement %D 2022 %R 10.19682/j.cnki.1005-8885.2022.0004 %J The Journal of China Universities of Posts and Telecommunications %P 63-72 %V 29 %N 2 %X
The sensing light source of the line scan camera cannot be fully exposed in a low light environment due to the extremely small number of photons and high noise, which leads to a reduction in image quality. A multi-scale fusion residual encoder-decoder (FRED) was proposed to solve the problem. By directly learning the end-to-end mapping between light and dark images, FRED can enhance the image's brightness with the details and colors of the original image fully restored. A residual block (RB) was added to the network structure to increase feature diversity and speed up network training. Moreover, the addition of a dense context feature aggregation module (DCFAM) made up for the deficiency of spatial information in the deep network by aggregating the context's global multi-scale features. The experimental results show that the FRED is superior to most other algorithms in visual effect and  quantitative evaluation of peak signa-to-noise ratio (PSNR) and structural similarity index measure (SSIM). For the factor that FRED can restore the brightness of images while representing the edge and color of the image effectively, a satisfactory visual quality is obtained under the enhancement of low-light.
%U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2022.0004