基于感知融合机制的渐进式去雾网络

doi:10.12305/j.issn.1001-506X.2023.11.07

Abstract

Abstract:

Intelligent assisted driving application scenarios require higher accuracy and real-time performance for image dehazing. This paper proposes a novel progressive dehaze network (PD-Net) based on perceptual fusion mechanism to improve the efficiency and accuracy of image dehaze. This method decomposes the task of degraded image recovery into multi-stage subtasks and uses lightweight subnetwork chunk to learn semantic information of different regions of the feature map to ensure image dehazing efficiency. On this basis, the cross-stage perception fusion module (PFM) is introduced based on the attention mechanism and guided filtering. It adaptively percepts semantic features and fuse the features in a cascading manner without losing image edges and texture details. Experimental results show that compared with the existing mainstream end-to-end dehazing models, the algorithm proposed in this paper has higher accuracy and real-time performance in processing outdoor images, with peaksignal to noise ratio (PSNR) improved by 0.93 dB compared with the best results available on the public synthetic object testing set (SOTS), which consumes only 72 ms when processing a single image.

Key words: image dehaze, attention mechanism, guided filter, perception fusion

CLC Number:

TP39

Chenghui QI, Dengyin ZHANG. Progressive image dehaze based on perceptual fusion[J]. Systems Engineering and Electronics, 2023, 45(11): 3419-3427.

Figures/Tables 10

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References 32

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算法	PSNR/dB	SSIM
DCP	19.31	0.814 8
CAP	18.49	0.798 2
DehazeNet	22.46	0.851 4
AOD-Net	20.29	0.876 5
GridDehazeNet	30.86	0.981 9
MSBDN	32.09	0.976 0
本文算法	33.02	0.986 5

损失函数	L_rec	L_per	L_col	PSNR/dB	SSIM
1	√	-	-	30.65	0.964 5
2	√	√	-	32.38	0.977 3
3	√	√	√	33.02	0.986 5

算法	Runtime
DCP	1.254
CAP	0.982
DehazeNet	1.031
AOD-Net	0.265
Grid-DNet	0.122
MSBDN	0.124
本文算法	0.072

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Progressive image dehaze based on perceptual fusion

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