Image Superresolution Based on Locally Adaptive Mixed-NormReportar como inadecuado

Image Superresolution Based on Locally Adaptive Mixed-Norm - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Journal of Electrical and Computer EngineeringVolume 2010 2010, Article ID 435194, 4 pages

Research ArticleDepartment of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan

Received 5 July 2009; Accepted 3 October 2009

Academic Editor: Kai-Kuang Ma

Copyright © 2010 Osama A. Omer and Toshihisa Tanaka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In a typical superresolution algorithm, fusion error modeling, including registration error and additive noise, has a great influence on the performance of the super-resolution algorithms. In this letter, we show that the quality of the reconstructed high-resolution image can be increased by exploiting proper model for the fusion error. To properly model the fusion error, we propose to minimize a cost function that consists of locally and adaptively weighted - and -norms considering the error model. Binary weights are used so as to adaptively select - or -norm, based on the local errors. Simulation results demonstrate that proposed algorithm can overcome disadvantages of using either - or -norm.

Autor: Osama A. Omer and Toshihisa Tanaka



Documentos relacionados