Treffer: Image denoising based on Kolmogorov structure function for a class of hierarchical image models

Title:
Image denoising based on Kolmogorov structure function for a class of hierarchical image models
Authors:
BARLIGA, Bogdan, TABUS, Loan, RISSANEN, Jorma, ASTOLA, Jaakko
Source:
Mathematical methods in pattern and image analysis (3-4 August 2005, San Diego, California, USA)Proceedings of SPIE. :1-591607
Publisher Information:
Bellingham, Wash: SPIE, 2005.
Publication Year:
2005
Physical Description:
print, 19 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, 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 image, Image analysis, Análisis imagen, Chaîne caractère, Character string, Cadena carácter, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Complexité calcul, Computational complexity, Complejidad computación, Complexité programme, Program complexity, Complejidad programa, Compression donnée, Data compression, Compresión dato, Compression image, Image compression, Compresión imagen, Description donnée, Data description, Décomposition fonction, Function decomposition, Descomposición función, Equation Kolmogorov, Kolmogorov equation, Ecuación Kolmogorov, Minimisation, Minimization, Minimización, Modèle donnée, Data models, Modélisation, Modeling, Modelización, Rapport signal bruit, Signal to noise ratio, Relación señal ruido, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Réduction bruit, Noise reduction, Reducción ruido, Théorie vitesse distorsion, Rate distortion theory, Traitement image, Image processing, Procesamiento imagen, Transformation ondelette, Wavelet transformation, Transformación ondita
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Signal Processing, Tampere University of Technology, P.O.Box 553, 33101 Tampere, Finland
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17707035
Rights:
Copyright 2006 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.17707035
Database:
PASCAL Archive

Weitere Informationen

Kolmogorov's structure function (KSF) is used in the algorithmic theory of complexity for describing the structure of a string by use of models (programs) of increasing complexity. Recently, inspired by the structure function, an extension of the minimum description length theory was introduced for achieving a decomposition of the total description of the data into a noise part and a model part, where the models are parametric distributions instead of programs, the code length necessary for the model part being restricted by a parameter. In this way a new rate-distortion type of curve is obtained, which may be further used as a general model of the data, quantifying the amount of noise left unexplained by models of increasing complexity. In this paper we present a complexity-noise function for a class of hierarchical image models in the wavelet transform domain, in the spirit of the Kolmogorov structure function. The minimization of the model description can be shown to have a form similar to one resulting from the minimization in the rate-distortion sense, and thus it will be achieved as in lossy image compression. As an application of the complexity-noise function introduced we study the image denoising problem and analyze the conditions under which the best reconstruction along the complexity-noise function is obtained.