Open AccessAn improved algorithm using weighted guided coefficient and union self‐adaptive image enhancement for single image haze removal
Author(s) -
Zhou Guangbin,
He Lifeng,
Qi Yong,
Yang Meimei,
Zhao Xiao,
Chao Yuyan
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12255
Subject(s) - haze , visibility , computer science , distortion (music) , algorithm , channel (broadcasting) , image restoration , constraint (computer aided design) , pixel , image (mathematics) , transmission (telecommunications) , artificial intelligence , gamma correction , computer vision , coefficient matrix , image processing , mathematics , optics , eigenvalues and eigenvectors , physics , telecommunications , amplifier , geometry , bandwidth (computing) , quantum mechanics , meteorology
Abstract The visibility of outdoor images is usually significantly degraded by haze. Existing dehazing algorithms, such as dark channel prior (DCP) and colour attenuation prior (CAP), have made great progress and are highly effective. However, they all suffer from the problems of dark distortion and detailed information loss. This paper proposes an improved algorithm for single‐image haze removal based on dark channel prior with weighted guided coefficient and union self‐adaptive image enhancement. First, a weighted guided coefficient method with sampling based on guided image filtering is proposed to refine the transmission map efficiently. Second, the k ‐means clustering method is adopted to calibrate the original image into bright and non‐bright colour areas and form a transmission constraint matrix. The constraint matrix is then marked by connected‐component labelling, and small bright regions are eliminated to form an atmospheric light constraint matrix, which can suppress the halo effect and optimize the atmospheric light. Finally, an adaptive linear contrast enhancement algorithm with a union score is proposed to optimize restored images. Experimental results demonstrate that the proposed algorithm can overcome the problems of image distortion and detailed information loss and is more efficient than conventional dehazing algorithms.