Self-Guided Image Dehazing Using Progressive Feature Fusion

We propose an effective image dehazing algorithm which explores useful information from the input hazy image itself as the guidance for the haze removal. The proposed algorithm first uses a deep pre-dehazer to generate an intermediate result, and takes it as the reference image due to the clear stru...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 31; pp. 1217 - 1229
Main Authors: Bai, Haoran, Pan, Jinshan, Xiang, Xinguang, Tang, Jinhui
Format: Journal Article
Language:English
Published: United States IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Atmospheric modeling
Estimation
Feature extraction
guidance information
Haze
Image color analysis
Image dehazing
Image restoration
Machine learning algorithms
Modules
pre-dehazer
progressive feature fusion
Scattering
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:We propose an effective image dehazing algorithm which explores useful information from the input hazy image itself as the guidance for the haze removal. The proposed algorithm first uses a deep pre-dehazer to generate an intermediate result, and takes it as the reference image due to the clear structures it contains. To better explore the guidance information in the generated reference image, it then develops a progressive feature fusion module to fuse the features of the hazy image and the reference image. Finally, the image restoration module takes the fused features as input to use the guidance information for better clear image restoration. All the proposed modules are trained in an end-to-end fashion, and we show that the proposed deep pre-dehazer with progressive feature fusion module is able to help haze removal. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods on the widely-used dehazing benchmark datasets as well as real-world hazy images.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2022.3140609