融合多分支残差与仿射变换的低照度图像增强.

The existing algorithms cannot deal with brightness, contrast, artifact and noise at the same time. This paper proposed the low-light enhancement network based on multi-branch residual and affine transformation. The key idea was to exert different modules to handle different tasks. Firstly, the illumination estimation module obtained the illumination variable of the low-light image. Secondly, it combined the illumination affine transformation module with the illumination encoding parameters to restore the illumination of the image. Finally, the detail reconstruction module fused more image details to obtain the final output. The experimental results show that the proposed method effectively enriches the image texture details, enhances brightness and contrast while suppressing the artifact and noise generated. Compared with popular methods, the results quantitatively and qualitatively prove that the method is better for low-light image enhancement. [ABSTRACT FROM AUTHOR]