NonLocal channel attention for nonhomogeneous image dehazing

Kareem Metwaly, Xuelu Li, Tiantong Guo, Vishal Monga

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The emergence of deep learning methods that complement traditional model-based methods has helped achieve a new state-of-the-art for image dehazing. Many recent methods design deep networks that either estimate the haze-free image (J) directly or estimate physical parameters in the haze model, i.e. ambient light (A) and transmission map (t) followed by using the inverse of the haze model to estimate the dehazed image. However, both kinds of methods fail in dealing with non-homogeneous haze images where some parts of the image are covered with denser haze and the other parts with shallower haze. In this work, we develop a novel neural network architecture that can take benefits of the aforementioned two kinds of dehazed images simultaneously by estimating a new quantity - a spatially varying weight map (w). w can then be used to combine the directly estimated J and the results obtained by the inverse model. In our work, we utilize a shared DenseNet-based encoder, and four distinct DenseNet-based decoders that estimate J, A, t, and w jointly. A channel attention structure is added to facilitate the generation of distinct feature maps of different decoders. Furthermore, we propose a novel dilation inception module in the architecture to utilize the non-local features to make up the missing information during the learning process. Experiments performed on challenging benchmark datasets of NTIRE'20 and NTIRE'18 demonstrate that the proposed method -namely, AtJwD- can outperform many state-of-the-art alternatives in the sense of quality metrics such as SSIM, especially in recovering images under non-homogeneous haze.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PublisherIEEE Computer Society
Pages1842-1851
Number of pages10
ISBN (Electronic)9781728193601
DOIs
StatePublished - Jun 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020 - Virtual, Online, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2020-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
Country/TerritoryUnited States
CityVirtual, Online
Period6/14/206/19/20

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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