Abstract: To address the issues of color distortion and low contrast in low-light images, and severe detail loss and excessive parameters of existing enhancement algorithms, a low-light image enhancement algorithm based on multi-scale depthwise separable convolution is proposed. Firstly, a multi-scale hybrid dilated convolution module is designed to expand the receptive field while addressing grid effects. Secondly, a multi-scale feature extraction module is designed to extract feature information at different scales. Finally, the two modules are used to fully integrate low-level spatial information with high-level semantic information for different-sized feature maps to obtain the final output. The use of depthwise separable convolution instead of standard convolution greatly reduces the network parameter count and computational cost. Experimental results show that the proposed algorithm effectively improves the brightness and contrast of images, reduces the number of model parameters, and restores image texture details and color well.

Key words: low-light image enhancement, depthwise separable convolution, dilated convolution, multi-scale, gridding problem