Spatially adaptive block-based super-resolution

Heng Su*, Liang Tang, Ying Wu, Daniel Tretter, Jie Zhou

*Corresponding author for this work

Research output: Contribution to journalArticle

46 Scopus citations

Abstract

Super-resolution technology provides an effective way to increase image resolution by incorporating additional information from successive input images or training samples. Various super-resolution algorithms have been proposed based on different assumptions, and their relative performances can differ in regions of different characteristics within a single image. Based on this observation, an adaptive algorithm is proposed in this paper to integrate a higher level image classification task and a lower level super-resolution process, in which we incorporate reconstruction-based super-resolution algorithms, single-image enhancement, and image/video classification into a single comprehensive framework. The target high-resolution image plane is divided into adaptive-sized blocks, and different suitable super-resolution algorithms are automatically selected for the blocks. Then, a deblocking process is applied to reduce block edge artifacts. A new benchmark is also utilized to measure the performance of super-resolution algorithms. Experimental results with real-life videos indicate encouraging improvements with our method.

Original languageEnglish (US)
Article number6008647
Pages (from-to)1031-1045
Number of pages15
JournalIEEE Transactions on Image Processing
Volume21
Issue number3
DOIs
StatePublished - Mar 1 2012

Keywords

  • Block based
  • motion registration error
  • spatially adaptive framework
  • super-resolution
  • super-resolution benchmark

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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