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
N1 - 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).
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
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U2 - 10.1016/j.jvcir.2017.02.016
DO - 10.1016/j.jvcir.2017.02.016
M3 - Article
AN - SCOPUS:85014171558
SN - 1047-3203
VL - 45
SP - 107
EP - 121
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
ER -