Exposedness-Based Noise-Suppressing Low-Light Image Enhancement

A noise-suppressing low-light image enhancement approach is proposed in this paper based on the extent of exposedness at each image pixel. To this end, a progressive, structure-aware exposedness estimation procedure is presented that quantifies local and global exposedness. These exposedness values...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2022-06, Vol.32 (6), p.3438-3451
Main Authors: Dhara, Sobhan Kanti, Sen, Debashis
Format: Article
Language:English
Subjects:
Ablation
Atmospheric modeling
Computational modeling
Estimation
Exposure
Image enhancement
Image quality
Light scattering
Lighting
local and global exposedness
Low-light image enhancement
Noise
Noise reduction
noise suppression
Pixels
State-of-the-art reviews
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A noise-suppressing low-light image enhancement approach is proposed in this paper based on the extent of exposedness at each image pixel. To this end, a progressive, structure-aware exposedness estimation procedure is presented that quantifies local and global exposedness. These exposedness values are leveraged to produce a locally smooth pixel-level map that signifies the required degrees of enhancement at image pixels. This map is subsequently used in an enhancement function, which satisfies a few important properties, to generate the enhanced image. Before the enhancement, inherent noise in the low-light image is diminished employing a detail-preserving, low gradient magnitude suppression method. Subjective and quantitative analysis of results on a wide variety of natural and synthetically generated low-light images from standard databases using PSNR, iRSE, SSIM, and measures of perceptual quality, natural image statistics and brightness preservation suggests that our approach in general outperforms the state-of-the-art. Ablation studies and further experiments show the importance of a few components of our approach, and that our approach is computationally fast.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2021.3113559