TY - JOUR
T1 - Color image demosaicking using inter-channel correlation and nonlocal self-similarity
AU - Chang, Kan
AU - Ding, Pak Lun Kevin
AU - Li, Baoxin
N1 - Funding Information:
This work was supported by the Natural Science Foundation of China via Grants 61401108 and 61261023 , the Natural Science Foundation of Guangxi Zhuang Autonomous Region via Grant 2013GXNSFBA019272 . The support provided by China Scholarship Council via Grant 201306665002 during a visit of K. Chang to Arizona State University is also acknowledged. B. Li was partially supported by the Natural Science Foundation via Grants 1135616 and 0845469 .
Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2015/11
Y1 - 2015/11
N2 - Color demosaicking is used to reconstruct full color images from incomplete color filter array samples captured by cameras with a single sensor array. In reconstructing natural-looking images, one key challenge is to model and respect the statistics of natural images. This paper presents a novel modeling strategy and an efficient color demosaicking algorithm. The approach starts with joint modeling of the color images, which supports simultaneous representation of inter-channel correlation and structural information in an image. The inter-channel correlation is explored by measuring the channel difference signals in the gradient domain, while the structural information is explored by nonlocal low-rank regularization. An efficient algorithm is then proposed to solve the joint formulation, by dividing the minimization problem into two sub-problems and solving them iteratively. The effectiveness of the proposed approach is demonstrated with extensive experiments on both noiseless and noisy datasets, with comparison with existing state-of-the-arts color demosaicking methods.
AB - Color demosaicking is used to reconstruct full color images from incomplete color filter array samples captured by cameras with a single sensor array. In reconstructing natural-looking images, one key challenge is to model and respect the statistics of natural images. This paper presents a novel modeling strategy and an efficient color demosaicking algorithm. The approach starts with joint modeling of the color images, which supports simultaneous representation of inter-channel correlation and structural information in an image. The inter-channel correlation is explored by measuring the channel difference signals in the gradient domain, while the structural information is explored by nonlocal low-rank regularization. An efficient algorithm is then proposed to solve the joint formulation, by dividing the minimization problem into two sub-problems and solving them iteratively. The effectiveness of the proposed approach is demonstrated with extensive experiments on both noiseless and noisy datasets, with comparison with existing state-of-the-arts color demosaicking methods.
KW - Color demosaicking
KW - Inter-channel correlation
KW - Joint modeling
KW - Nonlocal self-similarity
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U2 - 10.1016/j.image.2015.10.003
DO - 10.1016/j.image.2015.10.003
M3 - Article
AN - SCOPUS:84947928782
SN - 0923-5965
VL - 39
SP - 264
EP - 279
JO - Signal Processing: Image Communication
JF - Signal Processing: Image Communication
ER -