NDLI: Image dehazing with uneven illumination prior by dense residual channel attention network

Content Provider	IET Digital Library
Author	Yin, Shibai Xin, Jin Wang, Yibin Basu, Anup
Abstract	Existing dehazing methods based on convolutional neural networks estimate the transmission map by treating channel-wise features equally, which lacks flexibility in handling different types of haze information, leading to the poor representational ability of the network. Besides, the scene lights are predicted by an even illumination prior which does not work for a real situation. To solve these problems, the authors propose a dense residual channel attention network (DRCAN) for estimating the transmission map and use an image segmentation strategy to predict scene lights. Specifically, DRCAN is built based on the proposed dense residual block (DRB) and dense residual channel attention block (DRCAB). DRB extracts the hierarchical features with increasing receptive fields. DRCAB makes the network focus on the features containing heavy haze information. After the transmission map is estimated, fuzzy partition entropy combined with graph cuts is used to segment the transmission map into scene regions covered with varying scene lights. This strategy not only considers the fuzzy intensities of the low-contrast transmission map but also takes spatial correlation into account. Finally, a clear image is obtained by the transmission map and varying scene lights. Extensive experiments demonstrate that our method is comparable to most of existing methods.
Starting Page	3260
Ending Page	3272
Page Count	13
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0873
Journal	IET Image Processing
Publisher Date	2020-08-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Channel-wise Features Combinatorial Mathematics Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Dense Residual Channel Attention Block Dense Residual Channel Attention Network Entropy Feature Extraction Fuzzy Set Theory Haze Concentration Heavy Haze Information Image Classification Image Colour Analysis Image Dehazing Image Denoising Image Enhancement Image Recognition Image Representation Image Restoration Image Segmentation Image Segmentation Strategy Informative Feature Low-contrast Transmission Map Statistics Varying Scene Lights
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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