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 journalArticlepeer-review

40 Citations (Scopus)


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
Publication statusPublished - 2017 May 1

Bibliographical note

Funding Information:
This work was supported partly by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A1A10055037), and partly by the MSIP, Korea, under the Information Technology Research Center (ITRC) support program (IITP-2016-R2720-16-0007), supervised by the Institute for Information & communications Technology Promotion (IITP).

Publisher Copyright:
© 2017 Elsevier Inc.


  • 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


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