基于双分支通道先验和Retinex的低照度图像增强算法

doi:10.13229/j.cnki.jdxbgxb.20240148

Abstract

Abstract:

A low illumination image enhancement algorithm based on dual branch channel priors and Retinex is proposed to address the issues of local dimming， detail loss， and over enhancement in existing algorithms. Firstly， on the basis of Retinex， a dual branch bright dark prior feature guidance method is proposed， and a bright channel prior feature module and a dark channel prior feature module are designed to guide the network to suppress reflection component noise and improve lighting component brightness； Secondly， a pixel mixed attention mechanism module is designed to learn targeted features from three dimensions： channel， space， and pixel； Thirdly， a dark channel refractive index estimation module is designed to amplify image detail features. Finally， a mixed loss function is used to adjust brightness， contrast， noise， and color constraints. The experimental results on public datasets show that compared with 10 advanced algorithms， this algorithm improves image brightness while reducing color distortion and detail loss， achieving the best visual effects and quality indicators.

Key words: low light image, image enhancement, dual-branch channel retinexnet, pixel hybrid attention mechanism, channel prior features

CLC Number:

TP391

Yang LI,Xian-guo LI,Chang-yun MIAO,Sheng XU. Low⁃light image enhancement algorithm based on dual branch channel prior and Retinex[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 1028-1036.

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算法	PSNR↑	SSIM↑	CIEDE2000↓	NIQE↓
MSR-Net	14.69	0.49	20.17	5.38
LIME	16.00	0.52	20.39	3.77
MBLLEN	17.95	0.71	21.13	4.31
RetinexNet	17.57	0.64	19.32	4.54
KinD	20.23	0.77	17.39	4.24
KinD++	20.59	0.82	14.22	3.88
Zero-DCE	14.86	0.52	15.23	3.70
Zero-DCE++	16.42	0.60	15.19	3.49
URetinex-Net	21.17	0.81	13.92	4.02
PairLIE	19.51	0.74	14.23	4.33
本文	21.58	0.85	14.19	3.38

算法	参数量/M	计算量/G	运行时间/s
MSR-Net	0.68	318.18	0.168 8
MBLLEN	0.45	301.12	0.188 2
RetinexNet	0.84	587.47	0.321 8
KinD	8.16	567.23	0.386 8
KinD++	8.27	598.28	0.392 0
Zero-DCE	0.80	85.76	0.002 5
Zero-DCE++	0.08	19.06	0.001 5
URetinex-Net	0.36	940.74	0.014 4
PairLIE	0.34	368.27	0.012 3
本文	3.38	438.19	0.218 2

评估指标	baseline	w/o L_e	w/o L_i	w/o L_d	w/o L_c	w/o L_n	w/oL_s
PSNR↑	21.58	20.24	19.76	19.96	20.28	19.92	20.76
SSIM↑	0.858	0.800	0.821	0.831	0.792	0.819	0.838
NIQE↓	3.388	3.483	3.491	3.488	3.497	3.392	3.481

实验序号	BAM	PAM	PSNR↑	SSIM↑	NIQE↓
1	√	√	21.58	0.858	3.388
2	√	×	20.89	0.789	3.532
3	×	√	20.38	0.778	3.558
4	×	×	18.21	0.732	3.688

实验序号	BCPF	DCPF	DT	PSNR↑	SSIM↑	NIQE↓
1	√	√	√	21.58	0.858	3.388
2	√	√	×	20.78	0.833	3.397
3	√	×	×	19.63	0.798	3.482
4	×	√	√	19.89	0.808	3.428

Low⁃light image enhancement algorithm based on dual branch channel prior and Retinex

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