Context-aware joint dictionary learning for color image demosaicking

Kai Lung Hua, Shintami Chusnul Hidayati, Fang Lin He, Chia Po Wei, Yu Chiang Frank Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Most digital cameras are overlaid with color filter arrays (CFA) on their electronic sensors, and thus only one particular color value would be captured at every pixel location. When producing the output image, one needs to recover the full color image from such incomplete color samples, and this process is known as demosaicking. In this paper, we propose a novel context-constrained demosaicking algorithm via sparse-representation based joint dictionary learning. Given a single mosaicked image with incomplete color samples, we perform color and texture constrained image segmentation and learn a dictionary with different context categories. A joint sparse representation is employed on different image components for predicting the missing color information in the resulting high-resolution image. During the dictionary learning and sparse coding processes, we advocate a locality constraint in our algorithm, which allows us to locate most relevant image data and thus achieve improved demosaicking performance. Experimental results show that the proposed method outperforms several existing or state-of-the-art techniques in terms of both subjective and objective evaluations.

Original languageEnglish
Pages (from-to)230-245
Number of pages16
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Jul 2016
Externally publishedYes


  • Color demosaicking
  • Dictionary learning
  • Self-learning
  • Sparse representation


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