基于多层特征增强的双分支图像去雾算法

doi:10.16180/j.cnki.issn1007-7820.2025.06.005

Abstract

Abstract:

In view of the problems of residual haze, local detail loss, contour blur in traditional image dehazing algorithm, a double-branch image dehazing algorithm with multi-layer feature enhancement is proposed. Considering the problem of detail loss caused by extracting global information, the two-branch structure is used to fuse the global feature and local feature to compensate for the lost local detail feature, so as to restore high quality fog free image. Global branch fuses multi-scale global information by expanding convolution with different expansion rates. Local branches extract the local texture and color of the image through continuous local detail enhancement blocks. Experimental results show that compared with other algorithms, the proposed algorithm significantly improves the PSNR (Peak Signal-to-Noise Ratio) value on the composite image residing in the public haze image data set RESIDE. Experiments in real scenarios and ablation experiments have also proved the effectiveness of the proposed method.

Key words: multi scale, attention mechanism, feature enhancement, image dehazing, local feature, atmospheric scattering model, feature fusion, residual connection

CLC Number:

TP391.4

CHEN Qingjiang, YANG Shuang. A Dual Branch Image Dehazing Algorithm Based on Multi-Layer Feature Enhancement[J].Electronic Science and Technology, 2025, 38(6): 30-38.

Figures/Tables 16

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References 27

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算法	PSNR	SSIM
DCP	16.35	0.783 1
CAP	21.31	0.824 7
AOD-Net	19.69	0.839 5
RefineDNet	20.30	0.835 7
TCN	16.02	0.770 4
D⁴	19.20	0.864 3
USID-Net	17.45	0.769 9
本文算法	22.28	0.879 8

图像	评价指标	DCP	CAP	AOD-Net	RefineDNet	TCN	D⁴	USID-Net	本文算法
T₁	PSNR	16.891 4	22.361 1	25.632 6	21.753 6	18.669 1	23.401 3	24.303 3	23.797 8
T₁	SSIM	0.940 9	0.923 9	0.948 7	0.938 1	0.915 7	0.945 0	0.936 1	0.941 0
T₂	PSNR	16.893 1	21.887 7	20.004 4	16.898 2	16.655 0	23.419 5	22.592 9	24.649 8
T₂	SSIM	0.868 9	0.837 7	0.850 4	0.836 4	0.789 7	0.868 2	0.903 1	0.897 2
T₃	PSNR	17.855 3	18.314 2	18.863 5	18.007 6	17.982 1	18.009 7	21.197 3	22.509 1
T₃	SSIM	0.864 1	0.837 1	0.861 7	0.862 1	0.849 0	0.850 6	0.900 8	0.914 2
T₄	PSNR	13.394 1	19.940 4	18.381 4	17.177 1	18.351 3	23.219 2	28.276 8	22.609 3
T₄	SSIM	0.885 2	0.860 8	0.839 9	0.916 5	0.834 1	0.900 0	0.975 0	0.945 8

算法	I₁		I₂		P₁		P₃		T₁		T₃
算法	IE	SD	IE	SD	IE	SD	IE	SD	IE	SD	IE	SD
DCP	7.167 1	0.291 2	7.727 7	0.251 9	7.567 0	0.265 6	7.406 2	0.213 7	7.047 6	0.271 3	6.737 0	0.256 2
CAP	7.299 7	0.259 8	7.632 7	0.224 7	7.411 7	0.277 0	7.244 1	0.232 5	7.360 2	0.290 6	7.437 6	0.243 9
AOD-Net	7.169 3	0.269 9	7.847 0	0.257 9	7.446 1	0.278 9	7.256 3	0.228 9	6.765 4	0.335 4	6.621 9	0.237 7
RefineDNet	7.563 3	0.272 8	7.786 9	0.250 6	7.479 3	0.279 3	7.336 2	0.238 7	7.562 2	0.308 7	7.553 0	0.258 5
TCN	7.380 2	0.229 5	7.655 2	0.214 3	7.569 8	0.263 5	7.444 9	0.243 4	7.209 1	0.250 4	7.582 1	0.249 9
D⁴	7.551 5	0.298 9	7.907 7	0.273 8	7.547 9	0.3229	7.442 2	0.278 3	6.621 7	0.333 3	7.578 8	0.272 6
USID-Net	7.207 1	0.239 0	7.755 1	0.246 3	7.391 0	0.317 9	7.337 6	0.278 0	7.076 9	0.344 3	7.476 4	0.267 3
本文算法	7.539 4	0.295 8	7.910 6	0.270 0	7.573 4	0.332 3	7.134 1	0.272 9	7.462 5	0.325 4	7.459 7	0.272 6

算法	NRSS
DCP	0.385 5
CAP	0.391 7
AOD-Net	0.403 3
RefineDNet	0.464 8
TCN	0.431 0
D⁴	0.379 7
USID-Net	0.318 5
本文算法	0.420 1

模型	全局	局部	LF EM	DED AM	MSF EA	PSNR	SSIM
M₁	√	-	-	-	√	20.9981	0.7975
M₂	-	√	√	√	-	21.3714	0.8654
M₃	√	√	-	-	√	19.9993	0.7771
M₄	√	√	√	√	-	21.0829	0.8630
M₅	√	√	√	-	√	20.4410	0.8454
M₆	√	√	√	-	-	19.3702	0.8379
M₇	√	√	√	√	√	22.2784	0.8798

A Dual Branch Image Dehazing Algorithm Based on Multi-Layer Feature Enhancement

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Figures/Tables 16

References 27

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算法	P₃/s	R₄/s
DCP	0.0951	1.1916
CAP	0.1335	1.5315
AOD-Net	0.2914	0.5533
RefineDNet	2.6382	26.0547
TCN	0.7959	8.8780
D⁴	1.7635	2.8944
USID-Net	0.2616	10.6525
本文算法	1.5232	20.1137

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