Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis

Shenlong Wang, Lei Zhang, Yan Liang, Quan Pan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

431 Citations (Scopus)

Abstract

In various computer vision applications, often we need to convert an image in one style into another style for better visualization, interpretation and recognition; for examples, up-convert a low resolution image to a high resolution one, and convert a face sketch into a photo for matching, etc. A semi-coupled dictionary learning (SCDL) model is proposed in this paper to solve such cross-style image synthesis problems. Under SCDL, a pair of dictionaries and a mapping function will be simultaneously learned. The dictionary pair can well characterize the structural domains of the two styles of images, while the mapping function can reveal the intrinsic relationship between the two styles' domains. In SCDL, the two dictionaries will not be fully coupled, and hence much flexibility can be given to the mapping function for an accurate conversion across styles. Moreover, clustering and image nonlocal redundancy are introduced to enhance the robustness of SCDL. The proposed SCDL model is applied to image super-resolution and photo-sketch synthesis, and the experimental results validated its generality and effectiveness in cross-style image synthesis.
Original languageEnglish
Title of host publication2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Pages2216-2223
Number of pages8
DOIs
Publication statusPublished - 1 Oct 2012
Event2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012 - Providence, RI, United States
Duration: 16 Jun 201221 Jun 2012

Conference

Conference2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
CountryUnited States
CityProvidence, RI
Period16/06/1221/06/12

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this