Paper Info
Title
Sparse Coding Approach for Multi-Frame Image Super Resolution.
Abstract
An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. Since the estimated displacements are also regarded as a warping component of image degradation process, the matching results are directly utilized to generate low-resolution dictionary for sparse image representation. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. When there is only one low-resolution image, the proposed method works as a single-frame super-resolution method. The proposed method is shown to perform comparable or superior to conventional single- and multi-frame super-resolution methods through experiments using various real-world datasets.
Year
Venue
Field
2014
arXiv: Computer Vision and Pattern Recognition
Image warping,Image degradation,Pattern recognition,Computer science,Neural coding,Adaptive selection,Sparse image,Artificial intelligence,Superresolution
DocType
Volume
Citations 
Journal
abs/1402.3926
0
PageRank 
References 
Authors
0.34
12
3
Name
Order
Citations
PageRank
Toshiyuki Kato1192.10
Hideitsu Hino29925.73
Noboru Murata3855170.36