基于多尺度编码-解码神经网络的图像去雾算法

doi:10.13229/j.cnki.jdxbgxb.20231030

Abstract

Abstract:

Aiming at the problems of missing details， dim color and reduced brightness in the images collected by the image acquisition system in the foggy scene， a multi-scale encoding decoding neural network （MSAOD） model was proposed based on the AOD theory for image dehazing. The proposed network model was divided into three modules. The first module is the preprocessing module， which divides the input image into two parts for preprocessing. The second module is the backbone module， which extracts the features of the output of the first part through the multi-scale encoder decoder. The third module is the post-processing module， which maps the feature map. The experimental results show that this method is superior to the mainstream deep learning and traditional methods in image dehazing effect， and the image after dehazing is optimized in detail， color， brightness and so on.

Key words: digital image processing, image dehazing, atmospheric scattering model, deep learning, multi-scale network

CLC Number:

TP399

Yong WANG,Yu-xiao BIAN,Xin-chao LI,Chun-ming XU,Gang PENG,Ji-kui WANG. Image dehazing algorithm based on multiscale encoding decoding neural network[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3626-3636.

Figures/Tables 19

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方法	PSNR	SSIM
FVR	12.879 6	0.587 2
DCP	13.230 0	0.703 3
AL	18.819 5	0.862 7
ATM	7.977 3	0.694 0
Dehazednet	9.770 0	0.484 6
AOD-Net	19.704 8	0.751 8
本文方法	21.817 0	0.895 0

评分指标	FVR	ATM	DCP	AL	Dehazednet	AOD	MSAOD
Brenner	0.822 8	0.310 1	0.582 5	0.863 1	0.613 2	0.529 1	0.874 1
Laplacian	0.074 9	0.016 3	0.067 2	0.071 6	0.051 8	0.057 8	0.075 9
SMD	0.035 3	0.020 4	0.032 6	0.047 3	0.034 3	0.027 9	0.049 6
SMD2	2.145 8	0.795 7	1.868 3	2.436 1	1.252 3	1.305 4	2.616 5
Variance	4.841 0	2.972 9	7.416 7	8.239 1	5.364 3	6.758 3	8.207 3
Energy	1.059 6	0.307 9	1.049 8	1.053 8	0.742 7	0.509 4	0.793 0
Vollath	4.798 0	2.694 2	6.179 5	8.524 7	5.732 0	6.651 0	8.537 6
Entropy	4.856 3	4.545 4	4.453 8	5.077 8	4.135 8	4.969 7	5.023 1

评分指标	1	2	3	2+3	1+2+3	本文
PSNR	20.286	6.096	20.879	20.286	19.908	22.585
Brenner	0.655	0.678	0.621	0.632	0.938	1.182
Laplacian	0.039	0.041	0.021	0.036	0.052	0.054
Energy	0.821	0.612	0.871	0.686	0.836	0.896

Image dehazing algorithm based on multiscale encoding decoding neural network

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