Multiple dictionary learning for blocking artifacts reduction

Yi Wang, Fatih Porikli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We present a structured dictionary learning method to remove blocking artifacts without blurring edges or making any assumption over image gradients. Instead of a single overcomplete dictionary, we build multiple subspaces and impose sparsity on nonzero reconstruction coefficients when we project a given texture sample on each subspace separately. In case the texture matches to the dataset with which the subspace is trained, the corresponding response will be stronger and that subspace will be chosen to represent the texture. In this manner we compute the representations of all patches in the image and aggregate these to obtain the final image. Since the block artifacts are small in magnitude in comparison to actual image edges, aggregation efficiently removes the artifacts but keep the image gradients. We discuss the choices of subspace parameterizations and adaptation to given data. Our results on a large dataset of benchmark images demonstrate that the presented method provides superior results in terms of pixel-wise (PSNR) and perceptual (SSIM) measures.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1133-1136
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

Keywords

  • Deblocking
  • Dictionary Learning
  • JPEG

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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