Paper Title:
Super-Resolution Reconstruction of Video Sequences by GST-Driven Adaptive Filtering
  Abstract

A GST-driven spatially adaptive filter is developed in this paper based on the framework of non-local means (NLM) avoiding explicit motion estimation. Gradient Structure Tensor (GST) is introduced to express the underlying local image structural patterns, which drives the window function to yield adaptive scale and shape fitting for the local structure. This leads to patches with more similar gray-level and local structures being gathered for super-resolution estimation of image. Results on several test video sequences show that the proposed method is effective in providing super-resolution on general sequences and achieves improvement of performance on the compared method.

  Info
Periodical
Advanced Materials Research (Volumes 255-260)
Edited by
Jingying Zhao
Pages
2145-2149
DOI
10.4028/www.scientific.net/AMR.255-260.2145
Citation
L. Guo, L. Z. Lian, "Super-Resolution Reconstruction of Video Sequences by GST-Driven Adaptive Filtering", Advanced Materials Research, Vols. 255-260, pp. 2145-2149, 2011
Online since
May 2011
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Price
$32.00
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