Open AccessStatistical multidirectional line dark channel for single‐image dehazing
Author(s) -
Salazar Colores Sebastián,
MoyaSánchez Eduardo Ulises,
RamosArreguín JuanManuel,
CabalYépez Eduardo
Publication year - 2019
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/iet-ipr.2018.6403
Subject(s) - channel (broadcasting) , line (geometry) , computer science , image (mathematics) , artificial intelligence , computer vision , telecommunications , mathematics , geometry
Outdoor scenes often contain atmospheric degradation, such as fog or haze, which deteriorate the performance of tracking, autonomous driving and surveillance systems, among others, making dehazing methods an area of considerable interest. However, some dehazing techniques are computationally demanding, generating a trade‐off between time‐consumption and restoration quality. A new method is proposed for improving outdoor images taken with haze effects while making them less time‐consuming. The proposed method was inspired by the Radon transform and tailored for dehazing images by computing the dark channel, in addition to using statistical computations and a heuristic approach to avoid saturated areas. The results obtained were subjected to a reduced‐reference image quality dehazing assessment, a full‐reference metrics Structural SIMilarity (SSIM) index and peak signal‐to‐noise ratio (PSNR) over real‐world and synthetic outdoor images. The results demonstrate that the proposed method presents an adequate balance between new visible edges, increased gradient and saturated pixels, in addition to obtaining at least a 5% increase in the SSIM index and, a 16% increase in the PSNR index, as well as being 5.37 times faster than the four dehazing methods recently introduced in the literature.