Open AccessSingle image dehazing with bright object handling
Author(s) -
Riaz Irfan,
Fan Xue,
Shin Hyunchul
Publication year - 2016
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2015.0451
Subject(s) - computer science , pixel , haze , transmission (telecommunications) , block (permutation group theory) , brightness , thresholding , reliability (semiconductor) , artificial intelligence , channel (broadcasting) , computer vision , benchmark (surveying) , image (mathematics) , optics , mathematics , physics , geology , telecommunications , geometry , power (physics) , geodesy , quantum mechanics , meteorology
This study addresses the shortcomings of the dark channel prior (DCP). The authors propose a new and efficient method for transmission estimation with bright‐object handling capability. Based on the intensity value of a bright surface, they categorise DCP failures into two types: (i) obvious failure: occurs on surfaces that are brighter than ambient light. They show that, for these surfaces, altering the transmission value proportional to the brightness is better than the thresholding strategy; (ii) non‐obvious failure: occurs on surfaces that are brighter than the neighbourhood average haziness value. Based on the observation that the transmission of a surface is loosely connected to its neighbours, the local average haziness value is used to recompute the transmission of such surfaces. This twofold strategy produces a better estimate of block and pixel‐level haze thickness than DCP. To reduce haloes, a reliability map of block‐level haze is generated. Then, via reliability‐guided fusion of block‐ and pixel‐level haze values, a high‐quality refined transmission is obtained. Experimental results show that the authors’ method competes well with state‐of‐the‐art methods in typical benchmark images while outperforming these methods in more challenging scenarios. The authors’ proposed reliability‐guided fusion technique is about 60 times faster than other well‐known DCP‐based approaches.