AUTHOR=Wang Xinghao , Wang Qiang , Zhang Lei , Qu Yi , Yi Fan , Yu Jiayang , Liu Qiuhan , Xia Ruicong , Xu Ziling , Tong Sirong 

TITLE=DCENet-based low-light image enhancement improved by spiking encoding and convLSTM

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 18 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1297671

DOI=10.3389/fnins.2024.1297671

ISSN=1662-453X

ABSTRACT=Direct utilization of low-light images hinders downstream visual tasks. Traditional low-light enhancement (LLIE) methods such as retinex-based network require image pairs. A spiking-coding methodology called intensity to latency has been used to gradually acquire the structural characteristics of an image.And convLSTM has been used to connect the features. This study introduces simplified DCENet to achieve unsupervised LLIE and spiking coding mode of spiking neural network and applies the comprehensive coding features of convLSTM to improve subjective and objective effects of LLIE. In the ablation experiment for the proposed structure, the convLSTM structure was replaced by a convolutional neural network and the classical CBAM attention was introduced for comparison. Five objective evaluation metrics were compared with nine LLIE methods that currently exhibit strong comprehensive performance with PSNR, SSIM, MSE, UQI and VIFP exceeding the second place at 4.4 % (0.8 %), 3.9 % (17.2 %), 0 % (15 %), 0.1 % (0.2 %), and 4.3 % (0.9 %) on LOL and SCIE datasets. Further experiments of user study in five non-reference datasets were conducted to subjectively evaluate the effects depicted in the images.These experiments verified the remarkable performance of proposed method.