Paper Info
Title
A New Super-Resolution Algorithm Based on Areas Pixels and the Sampling Theorem of Papoulis
Abstract
In several application areas such as art, medicine, broadcasting and e-commerce, high-resolution images are needed. Super-resolution is the algorithmic process of increasing the resolution of an image given a set of displaced low-resolution, noisy and degraded images. In this paper, we present a new super-resolution algorithm based on the generalized sampling theorem of Papoulis and wavelet decomposition. Our algorithm uses an area-pixel model rather than a point-pixel model. The sampling theorem is used for merging a set of low-resolution images into a high-resolution image, and the wavelet decomposition is used for enhancing the image through efficient noise removing and high-frequency enhancement. The proposed algorithm is non-iterative and not time-consuming. We have tested our algorithm on multiple images and used the peak-to-noise ratio, the structural similarity index and the relative error as quality measures. The results show that our algorithm gives images of good quality.
Year
DOI
Venue
2008
10.1007/978-3-540-69812-8_10
ICIAR
Keywords
Field
DocType
multiple image,area-pixel model,sampling theorem,generalized sampling theorem,proposed algorithm,high-resolution image,low-resolution image,new super-resolution algorithm,degraded image,good quality,areas pixels,wavelet decomposition,e commerce,super resolution,low resolution,structural similarity,relative error,high frequency
Computer vision,Broadcasting,Wavelet decomposition,Super resolution algorithm,Pattern recognition,Computer science,Artificial intelligence,Pixel,Nyquist–Shannon sampling theorem,Generalized sampling,Merge (version control),Approximation error
Conference
Volume
ISSN
Citations 
5112
0302-9743
0
PageRank 
References 
Authors
0.34
15
3
Name
Order
Citations
PageRank
Alain Horé11699.54
François Deschênes2113.39
Djemel Ziou3139599.40