Modulus of continuity and its application in classifying the smoothness of images.
Pirzamanbein, Behnaz LU
(2011)
- Abstract
- The problems of de-blurring, de-noising, compression and segmentation are fundamental problems in image processing. Each of these problems can be formulated as a problem to find some approximation of an initial image. To find this approximation one needs to specify the approximation space and in what space the error between the image and its approximation should be calculated.
Using the space of Bounded Variation, BV, became very popular in the last decade. However it was later proved that for a rich variety of natural images it is more effective to use spaces of smooth functions that arecalled Besov spaces instead of BV. In the previous papers two methods for classifying the smoothness of images were suggested. The DeVore’s... (More) - The problems of de-blurring, de-noising, compression and segmentation are fundamental problems in image processing. Each of these problems can be formulated as a problem to find some approximation of an initial image. To find this approximation one needs to specify the approximation space and in what space the error between the image and its approximation should be calculated.
Using the space of Bounded Variation, BV, became very popular in the last decade. However it was later proved that for a rich variety of natural images it is more effective to use spaces of smooth functions that arecalled Besov spaces instead of BV. In the previous papers two methods for classifying the smoothness of images were suggested. The DeVore’s method based on the wavelet transform and Carasso’s method based on singular integrals are reviewed.
The classical definition of Besov spaces is based on the modulus of continuity. In this master thesis a new method is suggested for classifying the smoothness of images based on this definition. The developed method was applied to some images to classify the smoothness of them. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/90287fb3-5a43-448e-9e80-6b147483d89b
- author
- Pirzamanbein, Behnaz
LU
- supervisor
- publishing date
- 2011
- type
- Thesis
- publication status
- published
- subject
- pages
- 51 pages
- publisher
- Linnaeus University
- language
- English
- LU publication?
- no
- id
- 90287fb3-5a43-448e-9e80-6b147483d89b
- alternative location
- https://www.diva-portal.org/smash/get/diva2:421982/FULLTEXT01.pdf
- date added to LUP
- 2025-02-16 05:13:54
- date last changed
- 2025-04-04 15:20:38
@misc{90287fb3-5a43-448e-9e80-6b147483d89b,
abstract = {{The problems of de-blurring, de-noising, compression and segmentation are fundamental problems in image processing. Each of these problems can be formulated as a problem to find some approximation of an initial image. To find this approximation one needs to specify the approximation space and in what space the error between the image and its approximation should be calculated.<br/><br/>Using the space of Bounded Variation, BV, became very popular in the last decade. However it was later proved that for a rich variety of natural images it is more effective to use spaces of smooth functions that arecalled Besov spaces instead of BV. In the previous papers two methods for classifying the smoothness of images were suggested. The DeVore’s method based on the wavelet transform and Carasso’s method based on singular integrals are reviewed.<br/><br/>The classical definition of Besov spaces is based on the modulus of continuity. In this master thesis a new method is suggested for classifying the smoothness of images based on this definition. The developed method was applied to some images to classify the smoothness of them.}},
author = {{Pirzamanbein, Behnaz}},
language = {{eng}},
publisher = {{Linnaeus University}},
title = {{Modulus of continuity and its application in classifying the smoothness of images.}},
url = {{https://www.diva-portal.org/smash/get/diva2:421982/FULLTEXT01.pdf}},
year = {{2011}},
}