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Cosine-Driven Non-linear Denoising

Thürck, Daniel ; Kuijper, Arjan (2013):
Cosine-Driven Non-linear Denoising.
In: Lecture Notes in Computer Science (LNCS); 7950, pp. 245-254, Springer, Berlin; Heidelberg; New York, Image Analysis and Recognition, DOI: 10.1007/978-3-642-39094-4₂₈,
[Conference or Workshop Item]

Abstract

The Perona-Malik model is an effective but ill-posed model for denoising digital images by anisotropic diffusion. Instead of complex regularizations, we propose a new continuous model which is well-posed and show that it is nevertheless effective for denoising. In addition, an extension of our model offers the possibility of inducing a convergence for the discretization. A comparison to the original Perona-Malik model is carried out using an human vision-centered quality index which shows the improvements of our model when it comes to denoising.

Item Type: Conference or Workshop Item
Erschienen: 2013
Creators: Thürck, Daniel ; Kuijper, Arjan
Title: Cosine-Driven Non-linear Denoising
Language: English
Abstract:

The Perona-Malik model is an effective but ill-posed model for denoising digital images by anisotropic diffusion. Instead of complex regularizations, we propose a new continuous model which is well-posed and show that it is nevertheless effective for denoising. In addition, an extension of our model offers the possibility of inducing a convergence for the discretization. A comparison to the original Perona-Malik model is carried out using an human vision-centered quality index which shows the improvements of our model when it comes to denoising.

Series Name: Lecture Notes in Computer Science (LNCS); 7950
Publisher: Springer, Berlin; Heidelberg; New York
Uncontrolled Keywords: Digital image processing, Image processing, Partial differential equations, Image enhancement, Image restoration
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: Image Analysis and Recognition
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-3-642-39094-4₂₈
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