Fast deconvolution-based image super-resolution using gradient prior

Chun Yu Lin, Chih Chung Hsu, Chia Wen Lin*, Li Wei Kang

*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Single-image super-resolution (SR) is to reconstruct a high-resolution image from a low-resolution input image. Nevertheless, most SR algorithms are performed in an iterative manner and are therefore time-consuming. In this paper, we propose an iteration-free single-image SR algorithm based on fast deconvolution with gradient prior. Based on the prior calculated from the initially upsampled image via current approach (e.g., bicubic interpolation or example/learning-based approaches), we make the deconvolution process well-posed, which can be efficiently solved in FFT domain. Moreover, the proposed algorithm can be directly applied to video SR, where the temporal coherence can be automatically maintained. Experimental results demonstrate that the proposed method can simultaneously obtain significant acceleration and quality improvement over several existing SR methods.

Original languageEnglish
Title of host publication2011 IEEE Visual Communications and Image Processing, VCIP 2011
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 IEEE Visual Communications and Image Processing, VCIP 2011 - Tainan, Taiwan
Duration: 2011 Nov 62011 Nov 9

Publication series

Name2011 IEEE Visual Communications and Image Processing, VCIP 2011

Conference

Conference2011 IEEE Visual Communications and Image Processing, VCIP 2011
Country/TerritoryTaiwan
CityTainan
Period2011/11/062011/11/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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