loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hongwei Zheng and Olaf Hellwich

Affiliation: Computer Vision & Remote Sensing, Berlin University of Technology, Germany

ISBN: 978-972-8865-73-3

Keyword(s): Bayesian estimation, regularization, convex optimization, functions of bounded variation, linear growth functional, self-adjusting, parameter estimation, data-driven, hyperbolic conservation laws, image restoration.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Early Vision and Image Representation ; Enhancement and Restoration ; Image and Video Analysis ; Image Filtering ; Image Formation and Preprocessing ; Image Quality ; Implementation of Image and Video Processing Systems ; Mathematical Morphology ; Statistical Approach

Abstract: We present a novel variational regularization in the space of functions of Bounded Variation (BV) for adaptive data-driven image restoration. The discontinuities are important features in image processing. The BV space is well adapted for the measure of gradient and discontinuities. More over, the degradation of images includes not only random noises but also multiplicative, spatial degradations, i.e., blur. To achieve simultaneous image deblurring and denoising, a variable exponent linear growth functional on the BV space is extended in Bayesian estimation with respect to deblurring and denoising. The selection of regularization parameters is self-adjusting based on spatially local variances. Simultaneously, the linear and non-linear smoothing operators are continuously changed following the strength of discontinuities. The time of stopping the process is optimally determined by measuring the signal-to-noise ratio. The algorithm is robust in that it can handle images that are formed with different types of noises and blur. Numerical experiments show that the algorithm achieves more encouraging perceptual image restoration results. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.227.233.55

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Zheng H.; Hellwich O. and (2007). ADAPTIVE DATA-DRIVEN REGULARIZATION FOR VARIATIONAL IMAGE RESTORATION IN THE BV SPACE.In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 53-60. DOI: 10.5220/0002053900530060

@conference{visapp07,
author={Hongwei Zheng and Olaf Hellwich},
title={ADAPTIVE DATA-DRIVEN REGULARIZATION FOR VARIATIONAL IMAGE RESTORATION IN THE BV SPACE},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={53-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002053900530060},
isbn={978-972-8865-73-3},
}

TY - CONF

JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - ADAPTIVE DATA-DRIVEN REGULARIZATION FOR VARIATIONAL IMAGE RESTORATION IN THE BV SPACE
SN - 978-972-8865-73-3
AU - Zheng, H.
AU - Hellwich, O.
PY - 2007
SP - 53
EP - 60
DO - 10.5220/0002053900530060

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.