• Multi-scale MAP estimation of...

Result: Multi-scale MAP estimation of high-resolution images

Title:
Multi-scale MAP estimation of high-resolution images
Authors:
SHUBIN ZHAO
Source:
Advances in image and video technology (First pacific rim symposium, PSIVT 2006, Hsinchu, Taiwan, December 10-13, 2006)0PSIVT 2006. :1059-1066
Publisher Information:
Berlin; Heidelberg: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 12 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Optics, Optique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Analyse statistique, Statistical analysis, Análisis estadístico, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Basse résolution, Low resolution, Baja resolución, Estimation Bayes, Bayes estimation, Estimación Bayes, Haute résolution, High resolution, Alta resolucion, Image multiple, Multiple image, Imagen múltiple, Loi a priori, Prior distribution, Ley a priori, Modèle Markov caché, Hidden Markov model, Modelo Markov oculto, Méthode arborescente, Tree structured method, Método arborescente, Problème mal posé, Ill posed problem, Problema mal planteado, Reconstruction image, Image reconstruction, Reconstrucción imagen, Résolution image, Image resolution, Resolución imagen, Traitement image, Image processing, Procesamiento imagen, Transformation ondelette, Wavelet transformation, Transformación ondita
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Jiangsu Automation Research Institute, 42 East Hailian Road, Lianyungang, Jiangsu, 222006, China
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19008314
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19008314
Database:
PASCAL Archive

Further Information

In this paper, a multi-scale MAP algorithm for image super-resolution is proposed. It is well known that Reconstructing high-resolution(HR) images from multiple low-resolution(LR) images or a single one is an ill-posed problem. The main challenge is how to preserve edges in images while reducing noise. According to Bayesian approaches, which are popular and widely researched, solving this kind of problems is introducing prior knowledge about HR images as constraints and obtaining good HR images in some sense. In this paper, wavelet-domain prior distributions are concisely analyzed. And then, by introducing wavelet-domain Hidden Markov Tree-structured model(HMT) which accurately characterizes the statistics of most real-world images, reconstruction of HR images is reformulated as a multi-scale MAP estimation problem. For justification of this formulation, HMT is interpreted in the regularization framework, concisely and clearly. Experimental results are presented for assessment.