Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition

Jaemoon Lim, Minhyeok Heo, Chul Lee, Chang-Su Kim

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

A noisy low-light image enhancement algorithm based on structure-texture-noise (STN) decomposition is proposed in this work. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. More specifically, we first enhance the contrast of the structure image, by extending a 2D-histogram-based image enhancement scheme based on the characteristics of low-light images. Then, we reconstruct the texture image by retrieving residual texture components from the noise image and enhance it by exploiting the perceptual response of the human visual system (HVS). Experimental results on both synthetic and real-world images demonstrate that the proposed STN algorithm sharpens the texture and enhances the contrast more effectively than conventional algorithms, while providing robust performance under various noise and illumination conditions.

Original languageEnglish
Pages (from-to)107-121
Number of pages15
JournalJournal of Visual Communication and Image Representation
Volume45
DOIs
Publication statusPublished - 2017 May 1

Fingerprint

Textures
Decomposition
Image enhancement
Lighting

Keywords

  • Contrast enhancement
  • Denoising
  • Image enhancement
  • Noise removal
  • Structure-texture-noise decomposition
  • Texture enhancement
  • Texture retrieval

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition. / Lim, Jaemoon; Heo, Minhyeok; Lee, Chul; Kim, Chang-Su.

In: Journal of Visual Communication and Image Representation, Vol. 45, 01.05.2017, p. 107-121.

Research output: Contribution to journalArticle

@article{6ba8c71fc94946c196d6d031850ccd09,
title = "Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition",
abstract = "A noisy low-light image enhancement algorithm based on structure-texture-noise (STN) decomposition is proposed in this work. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. More specifically, we first enhance the contrast of the structure image, by extending a 2D-histogram-based image enhancement scheme based on the characteristics of low-light images. Then, we reconstruct the texture image by retrieving residual texture components from the noise image and enhance it by exploiting the perceptual response of the human visual system (HVS). Experimental results on both synthetic and real-world images demonstrate that the proposed STN algorithm sharpens the texture and enhances the contrast more effectively than conventional algorithms, while providing robust performance under various noise and illumination conditions.",
keywords = "Contrast enhancement, Denoising, Image enhancement, Noise removal, Structure-texture-noise decomposition, Texture enhancement, Texture retrieval",
author = "Jaemoon Lim and Minhyeok Heo and Chul Lee and Chang-Su Kim",
year = "2017",
month = "5",
day = "1",
doi = "10.1016/j.jvcir.2017.02.016",
language = "English",
volume = "45",
pages = "107--121",
journal = "Journal of Visual Communication and Image Representation",
issn = "1047-3203",
publisher = "Academic Press Inc.",

}

TY - JOUR

T1 - Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition

AU - Lim, Jaemoon

AU - Heo, Minhyeok

AU - Lee, Chul

AU - Kim, Chang-Su

PY - 2017/5/1

Y1 - 2017/5/1

N2 - A noisy low-light image enhancement algorithm based on structure-texture-noise (STN) decomposition is proposed in this work. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. More specifically, we first enhance the contrast of the structure image, by extending a 2D-histogram-based image enhancement scheme based on the characteristics of low-light images. Then, we reconstruct the texture image by retrieving residual texture components from the noise image and enhance it by exploiting the perceptual response of the human visual system (HVS). Experimental results on both synthetic and real-world images demonstrate that the proposed STN algorithm sharpens the texture and enhances the contrast more effectively than conventional algorithms, while providing robust performance under various noise and illumination conditions.

AB - A noisy low-light image enhancement algorithm based on structure-texture-noise (STN) decomposition is proposed in this work. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. More specifically, we first enhance the contrast of the structure image, by extending a 2D-histogram-based image enhancement scheme based on the characteristics of low-light images. Then, we reconstruct the texture image by retrieving residual texture components from the noise image and enhance it by exploiting the perceptual response of the human visual system (HVS). Experimental results on both synthetic and real-world images demonstrate that the proposed STN algorithm sharpens the texture and enhances the contrast more effectively than conventional algorithms, while providing robust performance under various noise and illumination conditions.

KW - Contrast enhancement

KW - Denoising

KW - Image enhancement

KW - Noise removal

KW - Structure-texture-noise decomposition

KW - Texture enhancement

KW - Texture retrieval

UR - http://www.scopus.com/inward/record.url?scp=85014171558&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014171558&partnerID=8YFLogxK

U2 - 10.1016/j.jvcir.2017.02.016

DO - 10.1016/j.jvcir.2017.02.016

M3 - Article

AN - SCOPUS:85014171558

VL - 45

SP - 107

EP - 121

JO - Journal of Visual Communication and Image Representation

JF - Journal of Visual Communication and Image Representation

SN - 1047-3203

ER -