Inpainting color images in learned dictionary

Marko Filipović, Ivica Kopriva, Andnej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)


Sparse representation of natural images over redundant dictionary enables solution of the inpainting problem. A major challenge, in this regard, is learning of a dictionary that is well adapted to the image. Efficient methods are developed for grayscale images represented in patch space by using, for example, K-SVD or independent component analysis algorithms. Here, we address the problem of patch space-based dictionary learning for color images. To this end, an image in RGB color space is represented as a collection of vectorized 3D patch tensors. This leads to the state-of-the-art results in inpainting random and structured patterns of missing values as it is demonstrated in the paper.

Original languageEnglish
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Number of pages5
Publication statusPublished - 2012
Externally publishedYes
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: 27 Aug 201231 Aug 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference20th European Signal Processing Conference, EUSIPCO 2012


  • color image
  • independent component analysis
  • inpainting
  • learned dictionary


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