Revisit Retinex Theory: Towards a Lightness-Aware Restorer for Underexposed Images
Joint Authors
Zhang, Lin
Zhu, Anqi
Shen, Ying
Zhao, Shengjie
Zhang, Huijuan
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-02
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
We investigate how to correct exposure of underexposed images.
The bottleneck of previous methods mainly lies in their naturalness and robustness when dealing with images with various exposure levels.
When facing well-exposed or extremely underexposed images, they may produce over- or underenhanced outputs.
In this paper, we propose a novel retinex-based approach, namely, LiAR (short for lightness-aware restorer).
The word “lightness-aware” refers to that the estimated illumination not only is a component to be adjusted but also serves as a measure that reflects the brightness of the scene, determining the degree of adjustment.
In this way, underexposed images can be restored adaptively according to their own brightness.
Given an image, LiAR first estimates its illumination map using a specially designed loss function which can ensure the result’s color consistency and texture richness.
Then adaptive correction is performed to get properly exposed output.
LiAR is based on internal optimization of the single test image and does not need any prior training, implying that it can adapt itself to different settings per image.
Additionally, LiAR can be easily extended to the video case due to its simplicity and stability.
Experiments demonstrate that facing images/videos with various exposure levels, LiAR can achieve robust and real-time correction with high contrast and naturalness.
The relevant code and collected data are publicly available at https://cslinzhang.github.io/LiAR-Homepage/.
American Psychological Association (APA)
Zhang, Lin& Zhu, Anqi& Shen, Ying& Zhao, Shengjie& Zhang, Huijuan. 2020. Revisit Retinex Theory: Towards a Lightness-Aware Restorer for Underexposed Images. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1193139
Modern Language Association (MLA)
Zhang, Lin…[et al.]. Revisit Retinex Theory: Towards a Lightness-Aware Restorer for Underexposed Images. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1193139
American Medical Association (AMA)
Zhang, Lin& Zhu, Anqi& Shen, Ying& Zhao, Shengjie& Zhang, Huijuan. Revisit Retinex Theory: Towards a Lightness-Aware Restorer for Underexposed Images. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1193139
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193139