research-article

Research and Implementation of Image Haze Removal Algorithm

Authors:

Ye PanAuthors Info & Claims

ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

Pages 56 - 60

https://doi.org/10.1145/3015166.3015187

Published: 21 November 2016 Publication History

Abstract

Haze has a great impact on the picture clarity, which cannot meetthe needs of high definition image areas. In this paper, image haze removal algorithms are studied, where the haze image are treated and restored using dark channel prior theory. Dark channel prior is an image Statistics Law -- within the vast majority of outdoor haze free image, there are always some points of a color channel whose value is close to zero; Using the law to establish a model, you can well recover haze free image.

References

[1]

Joung Youn Kim, Lee Sup Kim, Seung Ho Hwang. "An Advanced Contrast Enhancement Using Partially Overlapped Sub Block Histogram Equalization." IEEEE Transactions on Circuits and System for video Technology, 2001,11(3):475--484.

Digital Library

[2]

J. Alex Stark."Adaptive Image Contrast Enhancemen Using Generalizations of Histogram Equalization." IEEE Transactions on Image Processing, 2000, 9(4):889--896.

Digital Library

[3]

Z. Rahman, D. J. Jobson, G. A. Woodell. "Multiscale retinexfor color image enhancement."IEEE Image Processing, Proceedings, International Conference on, Sep 1996.

[4]

Neena. A, Aiswariya Raj, Rajesh Cherian Roy."Image Enhancement based on Stationary Wavelet Transform, Integer Wavelet Transform and Singular Value Decomposition". International Journal of Computer Applications, 2012, 58(10):30--35.

[5]

Manvi Malik, S. Majumder."Weather-predicting atmospheric modulation transfer function."IEEE Adva\nce Computing Conference (IACC), Feb. 2013.

[6]

J. Kopf et al."Deep photo: Model-basephotograph enhancement and viewing". ACM Trans. Graph., vol. 27, No. 5, pp. 1--116. Dec. 2008.

Digital Library

[7]

Dong-Hong Zhao, Lai-Sheng Wang. "A fourth order partial differential equation model from the Weber's total variation for image restoration". Advanced Computer Control (ICACC), Jan. 2011.

[8]

Robby T. Tan. "Visibility in Bad Weather from a Single Image". Proceedings of IEEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2008:23472354.

[9]

Raanan Fattal. "Single Image Dehazing". ACM Transactions on Graphics, 2008, 27(2):721--829.

Digital Library

[10]

Kaiming He, Jian Sun and Xiaoou Tang. "Single Image Haze Removal Using Dark Channel Prior". IEEE Transactions On Pattern Analysis And Machine Intelligence. Vol. 33, NO. 12, Dec., 2011.

Digital Library

[11]

Shuai Fang, Jiqing Zhan, Yang Cao and Ruizhong Rao. "Improved Single Image Dehazing Using Segmentation". IEEE 17th International Conference on Image Processing, Sep., 2010.

[12]

Hyungjo Park, Dubok Park, David K. Han and Hanseok Ko. "Single Image Haze Removal Using Novel Estimation of Atmospheric Light and Transmission". IEEE International Conference on Image Processing, Oct., 2014.

[13]

Yuan-Kai Wang, Ching-Tang Fan. "Single Image Dehazing by Multiscale Depth Fusion". IEEE Transactions on Image Processing, Vol. 23, NO. 1, Nov., 2014.

[14]

S.G. Narasimahn and S.K. Nayar, "Chromatic Framework for Vision in Bad Weather". Proc. Computer Vision and Pattern Recognition, vol. 1, pp. 598--605, June 2000.

[15]

Leo Levi and Richard H. Austing. "Errata: Tables of the Modulation Transfer Function of a Defocused Perfect Lens". Appl. Opt. 7, 2258_2--2258 (1968)

Cited By

Pal THalder MBarua S(2023)A transmission model based deep neural network for image dehazingMultimedia Tools and Applications10.1007/s11042-023-17010-483:13(39255-39281)Online publication date: 7-Oct-2023
https://doi.org/10.1007/s11042-023-17010-4
Gound RThepade S(2021)Removing Haze Influence from Remote Sensing Images Captured with Airborne Visible/ Infrared imaging Spectrometer by Cascaded Fusion of DCP, GF, LCC with AHE2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS51004.2021.9397060(658-664)Online publication date: 19-Feb-2021
https://doi.org/10.1109/ICCCIS51004.2021.9397060

Research and Implementation of Image Haze Removal Algorithm
1. Computing methodologies
  1. Artificial intelligence
  2. Computer graphics

Recommendations

l₂‐norm‐based prior for haze‐removal from single image

Climatic and atmospheric phenomena – such as haze, fog and smoke – may lead to deterioration of quality and poor scenic clarity of outdoor images. In computer graphics, the authors can model these images as a linear combination of scene radiance, medium ...
Haze Removal Algorithm Using Improved Restoration Model Based on Dark Channel Prior
Advances in Visual Informatics
Abstract
In recent years, with the rapid development of social economy and the people’s living standard, the awareness of security precaution is becoming increasingly important. However, in severe weather conditions, rain and haze have a large influence on ...
An Effective Scale-Aware Edge-Smoothing Weighting Constraint-Based Weighted Guided Image Filter for Single Image Dehazing
Abstract
This paper proposes a new effective scale-aware edge-smoothing weighting constraint-based weighted guided image filter (ESAESWC-WGIF) for single image dehazing. Edge-weighting constraint incorporated in this method is multi-scale and less ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICSPS 2016: Proceedings of the 8th International Conference on Signal Processing Systems

November 2016

235 pages

ISBN:9781450347907

DOI:10.1145/3015166

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICSPS 2016

ICSPS 2016: 8th International Conference on Signal Processing Systems

November 21 - 24, 2016

Auckland, New Zealand

Acceptance Rates

ICSPS 2016 Paper Acceptance Rate 46 of 83 submissions, 55%;

Overall Acceptance Rate 46 of 83 submissions, 55%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
86
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pal THalder MBarua S(2023)A transmission model based deep neural network for image dehazingMultimedia Tools and Applications10.1007/s11042-023-17010-483:13(39255-39281)Online publication date: 7-Oct-2023
https://doi.org/10.1007/s11042-023-17010-4
Gound RThepade S(2021)Removing Haze Influence from Remote Sensing Images Captured with Airborne Visible/ Infrared imaging Spectrometer by Cascaded Fusion of DCP, GF, LCC with AHE2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)10.1109/ICCCIS51004.2021.9397060(658-664)Online publication date: 19-Feb-2021
https://doi.org/10.1109/ICCCIS51004.2021.9397060

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents