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Proceedings Paper

A novel super-resolution image fusion algorithm based on improved PCNN and wavelet transform
Author(s): Na Liu; Yajun Song
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Paper Abstract

Super-resolution reconstruction technology is to explore new information between the under-sampling image series obtained from the same scene and to achieve the high-resolution picture through image fusion in sub-pixel level. The traditional super-resolution fusion methods for sub-sampling images need motion estimation and motion interpolation and construct multi-resolution pyramid to obtain high-resolution, yet the function of the human beings' visual features are ignored. In this paper, a novel resolution reconstruction for under-sampling images of static scene based on the human vision model is considered by introducing PCNN (Pulse Coupled Neural Network) model, which simplifies and improves the input model, internal behavior and control parameters selection. The proposed super-resolution image fusion algorithm based on PCNN-wavelet is aimed at the down-sampling image series in a static scene. And on the basis of keeping the original features, we introduce Relief Filter(RF) to the control and judge segment to overcome the effect of random factors(such as noise, etc) effectively to achieve the aim that highlighting interested object though the fusion. Numerical simulations show that the new algorithm has the better performance in retaining more details and keeping high resolution.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749621 (30 October 2009); doi: 10.1117/12.833925
Show Author Affiliations
Na Liu, Beijing Institute of Technology (China)
Yajun Song, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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