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Proceedings Paper

Multiframe super resolution based on block motion vector processing and kernel constrained convex set projection
Author(s): Miao Liu; Yuzhong Shen
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Paper Abstract

Even though substantial progress has been made in super resolution research, many issues regarding robust sub-pixel estimation and fast implementation of feature preserving restoration still exist. To obtain more reliable sub-pixel information, we proposed to correct mis-aligned sub-pixels by motion vector (MV) processing based on hierarchical block partition and weighted vector median filtering (WVMF). Two indices - relative displaced frame difference and motion vector similarity degree - are computed and compared with trained thresholds to classify the motion blocks into reliable and unreliable groups. Then the unreliable blocks are divided into four sub-blocks with their motion vector processed by WVMF based on the reliability information of their neighborhood blocks. To preserve the local features such as edge direction, strength as well as its spread region, anisotropic kernels are learned from local gradient fields to represent edge information. Finally, a kernel constrained projection is established for restoring high resolution frames. The experimental results show that the proposed algorithm preserves important features in the images and outperforms the traditional POCS method.

Paper Details

Date Published: 19 January 2009
PDF: 11 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72571J (19 January 2009); doi: 10.1117/12.805742
Show Author Affiliations
Miao Liu, Old Dominion Univ. (United States)
Yuzhong Shen, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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