Haze removal with fusion of local and non-local statistics

Jie Chen, Cheen Hau Tan, Lap Pui Chau*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Most of the outdoor images suffer from contrast degradation caused by fog and haze. Two statistical frameworks have been proposed in recent years that exploit local (dark channel prior) and non-local (haze-lines) characteristics of hazy images for the estimation of scene configurations and the restoration of scene albedo. Both frameworks show intrinsic limitations due to the basic assumptions they rely on. In this paper we propose a novel dehazing method that combines the advantages of local and non-local dehazing methods. Exploiting their complementary statistical properties, we use the local features to regulate the estimation of non-local haze-lines for a better final restoration at challenging regions. Both quantitative and qualitative results validate the effectiveness of our proposed method over state-of-the-art frameworks.

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings
PublisherIEEE
Number of pages5
ISBN (Electronic)9781728133201
DOIs
Publication statusPublished - Oct 2020
Event52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual, Online
Duration: 10 Oct 202021 Oct 2020
https://iscas2020.org/
https://ieeexplore.ieee.org/xpl/conhome/9179985/proceeding

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2020-October
ISSN (Print)0271-4310
ISSN (Electronic)2158-1525

Conference

Conference52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
CityVirtual, Online
Period10/10/2021/10/20
Internet address

Scopus Subject Areas

  • Electrical and Electronic Engineering

User-Defined Keywords

  • Dark channel prior
  • Haze line
  • Non-local

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