Finding the right exemplars for reconstructing single image super-resolution

Jiahuan Zhou, Ying Wu

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

Abstract

Exemplar-based methods have shown their potential in synthesizing novel but visually plausible contents for image super-resolution (SR), by using the implicit knowledge conveyed by the exemplar database. In practice, however, it is common that unwanted artifacts and low quality results are produced due to the using of inappropriate exemplars. How are the 'right' exemplars defined and identified? This fundamental issue has not be well addressed in these methods. This paper proposes a novel solution to this issue by learning a new distance metric in the LR space, such that affinity structure of the LR space under the new metric is as close to that of the HR space. Based on this learned best metric, appropriate exemplars can be identified. In addition, the proposed method is able to automatically determine the appropriate number of exemplars to use. Extensive experiments have shown that our method is able to handle regions with different properties and to obtain visually appealing super-resolution results with sharp details and smooth edges.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages1414-1418
Number of pages5
Volume2016-August
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period9/25/169/28/16

Keywords

  • Metric Learning
  • Right Exemplars
  • Super-Resolution

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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