Image superresolution based on locally adaptive mixed-norm
type: Item
instance of: scholarly article
title: Image superresolution based on locally adaptive mixed-norm
zbMATH Open document ID: 1229.94011
DOI: 10.1155/2010/435194
author: Osama A. Omer
author: Toshihisa Tanaka
published in: Journal of Electrical and Computer Engineering
publication date: 2010-07-22T00:00:00Z
review text: Summary: 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 \(L_{1}\)- and \(L_{2}\)-norms considering the error model. Binary weights are used so as to adaptively select \(L_{1}\)- or \(L_{2}\)-norm, based on the local errors. Simulation results demonstrate that proposed algorithm can overcome disadvantages of using either \(L_{1}\)- or \(L_{2}\)-norm.
Mathematics Subject Classification ID: 94A08
zbMATH DE Number: 5759304
Wikidata QID: Q58652603
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2010/435194
OpenAlex ID: W2087657713
cites work: A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur
description: scientific article; zbMATH DE number 5759304