For light-weight low level light intensifying network,blurred image issue caused by inconsistent light intensifying degree in different area can occur when Zero-DCE handles the low level light image with a bigger brightness variation range.This paper introduces a self-adaptive loss function based on γtransform,on the basis of the original loss function,decreases the sensitivity of the network on image exposure difference and dramatically improves the low level light intensifying effect.In this method,CBAM module is added into the convolutional neural network(CNN)to increase the expression ability of the network to low level light image feature,in addition,the logarithm distance between the average value of gray level of the network intensifying image and the average value of intensifying feature image is selected as γ transformed self-adaptive factor,and finally,the gray level parameter distance between network intensifying image and γ transformed image is calculated.The experiment shows that the performance of this method is dramatically improved comparing to the original network,in which in aspect of image evaluation index,the error mean square is increased by 9.7％,the peak signal to noise ratio is increased by 13.8％,and the structure similarity is increased by 6.7％.
关键词
图像增强/自适应/伽马变换/曝光损失函数/卷积神经网络(CNN)/注意力机制
Key words
image enhancement/adaptive/Gamma transform/exposure loss function/convolutional neu-ral network(CNN)/attention mechanism
维普
万方数据