Open AccessPerceptually motivated enhancement method for non‐uniformly illuminated images
Author(s) -
Pu Tian,
Wang Shuhang
Publication year - 2018
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.2017.0259
Subject(s) - brightness , artificial intelligence , luminance , computer vision , computer science , contrast (vision) , visibility , receptive field , transfer function , human visual system model , optical transfer function , image (mathematics) , optics , physics , electrical engineering , engineering
Non‐uniformly illuminated images often suffer from low visibility in dark areas. Traditional methods usually enhance non‐uniformly illuminated images by bringing out the details in the dark areas, but easily result in over‐enhancement. Motivated by the Weber contrast model, the authors propose a perceptually inspired image enhancement method, which treats an image as a product of a luminance mapping (LM) transfer function and a contrast measure (CM) transfer function. The contribution of this proposed method is two‐fold. Firstly, they propose a progressive LM transfer function based on the sensitivity of the human visual system to emphasise changes at low brightness level and attenuates changes at high brightness levels. Secondly, they introduce a CM transfer function, which is based on a special implementation of a neural model of the human visual receptive field, to improve local intensity contrast. Experimental comparisons with some state‐of‐the‐art methods show that the proposed method can achieve both contrast enhancement and visual fidelity preservation.