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Proceedings Paper

Subpixel motion estimation for multiframe resolution enhancement
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Paper Abstract

Multiframe resolution enhancement algorithms are used to estimate a high-resolution video still (HRVS) from several low-resolution frames, provided that objects within the image sequence move with subpixel increments. A Bayesian multiframe enhancement algorithm is presented to compute an HRVS using the spatial information present within each frame as well as the temporal information present due to object motion between frames. However, the required subpixel- resolution motion vectors must be estimated from low- resolution and noisy video frames, resulting in an inaccurate motion field which can adversely impact the quality of the enhanced image. Several subpixel motion estimation techniques are incorporated into the Bayesian multiframe enhancement algorithm to determine their efficacy in the presence of global data transformations between frames and independent object motion. Visual and quantitative comparisons of the resulting high-resolution video stills computed from two video frames and the corresponding estimated motion fields show that the eight- parameter projective motion model is appropriate for global scene changes, while block matching and Horn-Schunck optical flow estimation each have their own advantages and disadvantages when used to estimate independent object motion.

Paper Details

Date Published: 10 January 1997
PDF: 12 pages
Proc. SPIE 3024, Visual Communications and Image Processing '97, (10 January 1997); doi: 10.1117/12.263212
Show Author Affiliations
Richard R. Schultz, Univ. of North Dakota (United States)
Li Meng, Univ. of North Dakota (United States)
Robert L. Stevenson, Univ. of Notre Dame (United States)


Published in SPIE Proceedings Vol. 3024:
Visual Communications and Image Processing '97
Jan Biemond; Edward J. Delp, Editor(s)

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