Open AccessFusion‐based simultaneous estimation of reflectance and illumination for low‐light image enhancement
Author(s) -
Parihar Anil Singh,
Singh Kavinder,
Rohilla Hrithik,
Asnani Gul
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/ipr2.12114
Subject(s) - artificial intelligence , computer vision , color constancy , computer science , reflectivity , image (mathematics) , image fusion , naturalness , light field , optics , physics , quantum mechanics
Abstract Low‐light image enhancement is a challenging field in image processing. Retinex‐based methods perform well for low‐light images. However, reflectance and illumination estimation is an ill‐posed problem. This paper presents a new framework for the simultaneous estimation of reflectance and illumination for low‐light image enhancement. The algorithm estimates multiple instances of illumination and reflectance and blends them to estimate the final components. The proposed approach uses multi‐scale fusion for illumination estimation and naive fusion for reflectance estimation. Extensive experimentation and analysis with a large set of low‐light images validates the performance of the proposed approach. The comparison shows the superiority of the proposed approach over most of the existing low‐light image enhancement methods. The proposed method provides colour constancy in low‐light image enhancement and preserves the naturalness of the image.