Multi-frame image super resolution based on sparse coding. (June 2015)

• Record Type: Journal Article
• Authors: Kato, Toshiyuki; Hino, Hideitsu; Murata, Noboru
• Is Part Of: Neural networks. Volume 66(2015:Jun.)
• Page Start: 64
• Page End: 78
• Publication Date: 2015-06
• Subjects: Image super resolution -- Multi-frame super-resolution -- Sparse coding; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.02.009
• Languages: English
• ISSNs: 0893-6080

Abstract

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, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. 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. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images.