Dense '123' color enhancement dehazing network

Tiantong Guo, Venkateswararao Cherukuri, Vishal Monga

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

2 Scopus citations

Abstract

Single image dehazing has gained much attention recently. A typical learning based approach uses example hazy and clean image pairs to train a mapping between the two. Of the learning based methods, those based on deep neural networks have shown to deliver state of the art performance. An important aspect of recovered image quality is the color information, which is severely compromised when the image is corrupted by very dense haze. While many different network architectures have been developed for recovering dehazed images, an explicit attention to recovering individual color channels with a design that ensures their quality has been missing. Our proposed work, focuses on this issue by developing a novel network structure that comprises of: a common DenseNet based feature encoder whose output branches into three distinct DensetNet based decoders to yield estimates of the R, G and B color channels of the image. A subsequent refinement block further enhances the final synthesized RGB/color image by joint processing of these color channels. Inspired by its structure, we call our approach the One-To-Three Color Enhancement Dehazing (123-CEDH) network. To ensure the recovery of physically meaningful and high quality color channels, the main network loss function is further regularized by a multi-scale structural similarity index term as well as a term that enhances color contrast. Experiments reveal that 123-CEDH has the ability to recover color information at early training stages (i.e. in the first few epochs) vs. other highly competitive methods. Validation on the benchmark datasets of the NTIRE'19 and NTIRE'18 dehazing challenges reveals the 123-CEDH to be one of the Top-3 methods based on results released in the NTIRE'19 competition.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
PublisherIEEE Computer Society
Pages2131-2139
Number of pages9
ISBN (Electronic)9781728125060
DOIs
StatePublished - Jun 2019
Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States
Duration: Jun 16 2019Jun 20 2019

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2019-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
CountryUnited States
CityLong Beach
Period6/16/196/20/19

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Guo, T., Cherukuri, V., & Monga, V. (2019). Dense '123' color enhancement dehazing network. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 (pp. 2131-2139). [9025594] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2019.00266