Double sparsity for multi-frame super resolution

Toshiyuki Kato, Hideitsu Hino*, Noboru Murata

*この研究の対応する著者

研究成果: Article査読

11 被引用数 (Scopus)

抄録

A number of image super resolution algorithms based on the sparse coding have successfully implemented multi-frame super resolution in recent years. In order to utilize multiple low-resolution observations, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The proposed formulation not only has a mathematically interesting structure, called the double sparsity, but also yields comparable or improved numerical performance to conventional methods.

本文言語English
ページ(範囲)115-126
ページ数12
ジャーナルNeurocomputing
240
DOI
出版ステータスPublished - 2017 5月 31

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 認知神経科学
  • 人工知能

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