Incremental Image Dehazing Algorithm Based on Multiple Transfer Attention

doi:10.16182/j.issn1004731x.joss.22-1538

Abstract

Abstract:

In order to improve the processing ability of the depth-neural network dehazing algorithm to the supplementary data set, and to make the network differently process the image features of different importance to improve the dehazing ability of the network, an incremental dehazing algorithm based on multiple migration of attention is proposed. The teacher's attention generation network in the form of Encoder-Decoder extracts the multiple attention of labels and haze, which is used it as the label of the characteristic migration media network to constrain the network training to form the migration media attention as close as possible to the teacher's attention. The attention is integrated into the characteristics of the student's dehazing network to improve the dehazing ability of the student's dehazing network. The incremental training method is used to improve the processing ability of students' dehazing network to the supplementary data set. The results show that the proposed algorithm has good processing ability on ITS, OTS and real hazy images, and has good dehazing effect while ensuring the integrity of pixel structure and color distortion of the dehazing image. The image processed by the algorithm is superior to the contrast algorithm in subjective visual effect and objective evaluation index.

Key words: deep learning, image dehazing, transfer attention, incremental training

CLC Number:

TP391.9

Wei Jinyang, Wang Keping, Yang Yi, Fei Shumin. Incremental Image Dehazing Algorithm Based on Multiple Transfer Attention[J]. Journal of System Simulation, 2024, 36(4): 969-980.

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算法	SSIM	PSNR/dB
DCP	0.81	17.04
DehazeNet	0.84	20.55
AOD-Net	0.80	18.42
GCANet	0.89	27.50
FFA-Net	0.73	14.36
所提算法(增量前) 所提算法(增量后)	0.90 0.90	27.52 26.74

算法	SSIM	PSNR/dB
DCP DehazeNet AOD-Net	0.83 0.83 0.83	16.94 22.25 19.38
GCANet	0.89	22.74
FFA-Net	0.87	22.09
所提算法(增量前) 所提算法(增量后)	0.85 0.90	21.91 26.68

图像	DCP	DehazeNet	AOD-Net	GCANet	FFA-Net	本文
1	978	2 404	2 035	3 034	2 610	3 106
2	2 828	4 171	2 717	1 510	3 530	4 259
3	1 418	1 325	1 023	1 193	627	2 280
4	1 701	3 404	3 775	4 833	2 151	5 854

图像	DCP	DehazeNet	AOD-Net	GCANet	FFA-Net	本文
1	12.30	12.19	12.04	12.91	11.36	13.00
2	11.71	12.06	11.40	11.80	10.72	12.86
3	11.63	11.65	11.21	11.48	10.32	12.42
4	11.00	11.84	11.86	12.50	11.30	12.55

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