基于DBAFFNet的低照度图像增强

doi:10.3969/j.issn.0255-8297.2023.03.009

Abstract

Abstract: To solve the problems of color cast, detail loss and noise amplification after lowlight image enhancement, an improved low-light image enhancement method is proposed based on dual-branch adaptive feature fusion network (DBAFFNet). Firstly, the adaptive feature fusion (AFF) module is designed to fuse more details and color information into deep features. Secondly, the channel and spatial attention (CASA) module is established to focus on the restoration of image details and color. Finally, a Poisson-Retinex loss function based on Retinex theory is designed to suppress noise of the image, thereby improving the enhancement effect of images. The results of subjective and objective comparisons on multiple datasets demonstrate that the proposed method not only restores the color and details of the enhanced image, but also suppress the noise better, and achievea good enhancement effect.

Key words: low-light image enhancement, adaptive feature fusion module, attention module, Retinex

CLC Number:

TP391

LUO Fan, XIONG Bangshu, YU Lei, WANG Wanling. Low-Light Image Enhancement Based on DBAFFNet[J]. Journal of Applied Sciences, 2023, 41(3): 476-487.

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Low-Light Image Enhancement Based on DBAFFNet

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