Single Image Dehazing Using Sparse Contextual Representation

被引：0

作者：

Qin, Jing ^{[1
]}

Chen, Liang ^{[2
]}

Xu, Jian ^{[3
]}

Ren, Wenqi ^{[4
]}

机构：

[1] Suzhou Chien Shiung Inst Technol, Sch Elect Informat, Suzhou 215411, Peoples R China

[2] Shaoxing Univ, Comp Sci Engn Dept, Shaoxing 312000, Peoples R China

[3] Qujing Normal Univ, Sch Informat Engn, Qujing 655011, Peoples R China

[4] Chinese Acad Sci, State Key Labortory Informat Secur, Beijing 100086, Peoples R China

来源：

ATMOSPHERE | 2021年 / 12卷 / 10期

基金：

中国国家自然科学基金;

关键词：

image dehazing; sparse representation; contextual regularization; transmission estimation; HAZE;

D O I：

10.3390/atmos12101266

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block artifacts and halos produced by only using dark channel prior without soft matting as the transmission is not always constant in a local patch. A novel way to use dictionary is proposed to smooth an image and generate the sharp dehazed result. Experimental results demonstrate that our proposed method performs favorably against the state-of-the-art dehazing methods and produces high-quality dehazed and vivid color results.</p>

引用

页数：11