58
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation

      Preprint

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A framework of demosaicing and superresolution for color filter array (CFA) via residual image reconstruction and sparse representation is presented.Given the intermediate image produced by certain demosaicing and interpolation technique, a residual image between the final reconstruction image and the intermediate image is reconstructed using sparse representation.The final reconstruction image has richer edges and details than that of the intermediate image. Specifically, a generic dictionary is learned from a large set of composite training data composed of intermediate data and residual data. The learned dictionary implies a mapping between the two data. A specific dictionary adaptive to the input CFA is learned thereafter. Using the adaptive dictionary, the sparse coefficients of intermediate data are computed and transformed to predict residual image. The residual image is added back into the intermediate image to obtain the final reconstruction image. Experimental results demonstrate the state-of-the-art performance in terms of PSNR and subjective visual perception.

          Related collections

          Author and article information

          Journal
          2012-09-29
          2013-07-03
          Article
          1210.0115
          a5f07aa8-a800-4708-874a-7465771448c5

          http://creativecommons.org/licenses/by-nc-sa/3.0/

          History
          Custom metadata
          the paper has been accepted by a journal
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

          Comments

          Comment on this article