Color image demosaicking using inter-channel correlation and nonlocal self-similarity

Kan Chang, Pak Lun Kevin Ding, Baoxin Li

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)264-279
Number of pages16
JournalSignal Processing: Image Communication
StatePublished - Nov 2015


  • Color demosaicking
  • Inter-channel correlation
  • Joint modeling
  • Nonlocal self-similarity

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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