Ensemble dehazing networks for non-homogeneous haze

Mingzhao Yu, Venkateswararao Cherukuri, Tiantong Guo, Vishal Monga

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

1 Scopus citations

Abstract

Image dehazing is one of the most challenging imaging inverse problems. Although deep learning methods produce compelling results, one of the most crucial practical challenge is that of non-homogeneous haze, which remains an open problem. To address this challenge, we propose 3 models that are inspired by ensemble techniques. First, we propose a DenseNet based single-encoder four-decoders structure denoted as EDN-3J, wherein among the four decoders, three of them output estimates of dehazed images (J1, J2, J3) that are then weighted and combined via weight maps learned by the fourth decoder. In our second model called EDN-AT, the single-encoder four-decoders structure is maintained while three decoders are transformed to jointly estimate two physical inverse haze models that share a common transmission map t with two distinct ambient light maps (A1, A2). The two inverse haze models are then weighted and combined for the final dehazed image. To endow two sub-models flexibility and to induce capability of modeling non-homogeneous haze, we apply attention masks to ambient lights. Both the weight maps and attention maps are generated from the fourth decoder. Finally, in contrast to the above two ensemble models, we propose an encoder-decoder-U-net structure called EDN-EDU, which is a sequential hierarchical ensemble of two different dehazing networks with different modeling capacities. Experiments performed on challenging benchmark image datasets of NTIRE'20 and NTIRE'19 demonstrate that the proposed models outperform many state-of-the-art methods and this fact is particularly demonstrated in the NTIRE-2020 contest where the EDN-AT model achieves the best result in the sense of the perceptual quality metric LPIPS.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020
PublisherIEEE Computer Society
Pages1832-1841
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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