article · 2015

Selective Image Super-Resolution

Ju Suna, Qiang Chenb, Shuicheng Yanb, Loong-fah Cheongb

Cited by 0Abstract and details only

Abstract

In this paper we propose a vision system that performs image Super Resolu-tion (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed “blind ” resolu-tion recovery to the entire image area. By comparison, we advocate example-based selective SR whereby selectivity is exemplified in three aspects: region selectivity (SR only at object regions), source selectivity (object SR with trained object dictionaries), and refinement selectivity (object boundaries refinement using matting). The proposed system takes over-segmented low-resolution images as inputs, assimilates recent learning techniques of sparse coding (SC) and grouped multi-task lasso (GMTL), and leads eventually to a framework for joint figure-ground separation and interest object SR. The efficiency of our framework is manifested in our experiments with subsets of the VOC2009 and MSRC datasets. We also demonstrate several interesting vision applications that can build on our system.

Cite this paper

APA 7

Suna, J., Chenb, Q., Yanb, S., & Cheongb, L.-F. (2015). Selective Image Super-Resolution.

MLA 9

Suna, Ju, et al. “Selective Image Super-Resolution.” 2015.

Chicago

Suna, Ju, Qiang Chenb, Shuicheng Yanb, and Loong-fah Cheongb. 2015. “Selective Image Super-Resolution.”

Harvard

Suna, J. et al. (2015) ‘Selective Image Super-Resolution’.

BibTeX

@article{suna2015selective,
  title = {{Selective Image Super-Resolution}},
  author = {Suna, Ju and Chenb, Qiang and Yanb, Shuicheng and Cheongb, Loong-fah},
  year = {2015}
}

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