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- Lukic, TiborUniversity of Novi Sad, Faculty of technical sciences (author)
Regularized image denoising based on spectral gradient optimization
- Article/chapterEnglish2011
Publisher, publication year, extent ...
- 2011-07-27
- IOP Publishing,2011
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:uu-157026
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-157026URI
- https://doi.org/10.1088/0266-5611/27/8/085010DOI
- https://res.slu.se/id/publ/57824URI
Supplementary language notes
- Language:English
- Summary in:English
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- SwePubSwePub
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Notes
- Image restoration methods, such as denoising, deblurring, inpainting, etc, are often based on the minimization of an appropriately defined energy function. We consider energy functions for image denoising which combine a quadratic data-fidelity term and a regularization term, where the properties of the latter are determined by a used potential function. Many potential functions are suggested for different purposes in the literature. We compare the denoising performance achieved by ten different potential functions. Several methods for efficient minimization of regularized energy functions exist. Most are only applicable to particular choices of potential functions, however. To enable a comparison of all the observed potential functions, we propose to minimize the objective function using a spectral gradient approach; spectral gradient methods put very weak restrictions on the used potential function. We present and evaluate the performance of one spectral conjugate gradient and one cyclic spectral gradient algorithm, and conclude from experiments that both are well suited for the task. We compare the performance with three total variation-based state-of-the-art methods for image denoising. From the empirical evaluation, we conclude that denoising using the Huber potential (for images degraded by higher levels of noise; signal-to-noise ratio below 10 dB) and the Geman and McClure potential (for less noisy images), in combination with the spectral conjugate gradient minimization algorithm, shows the overall best performance.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
- TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
- ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
- NATURVETENSKAP Matematik Annan matematik hsv//swe
- NATURAL SCIENCES Mathematics Other Mathematics hsv//eng
- NATURVETENSKAP Fysik Atom- och molekylfysik och optik hsv//swe
- NATURAL SCIENCES Physical Sciences Atom and Molecular Physics and Optics hsv//eng
- Image analysis
- Bildanalys
- Computerized Image Processing
- Datoriserad bildbehandling
Added entries (persons, corporate bodies, meetings, titles ...)
- Lindblad, JoakimSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Uppsala universitet,Centrum för bildanalys,Bildanalys och människa-datorinteraktion,Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia,,Centre for Image Analysis,University of Novi Sad(Swepub:slu)51900 (author)
- Sladoje, NatasaUppsala universitet,Centrum för bildanalys,Bildanalys och människa-datorinteraktion,Mathematical Institute, Serbian Academy of Sciences and Arts, Belgrade, Serbia(Swepub:uu)namat934 (author)
- University of Novi Sad, Faculty of technical sciencesCentrum för bildanalys (creator_code:org_t)
- Sveriges lantbruksuniversitet
Related titles
- In:Inverse Problems: IOP Publishing27:8, s. 085010:1-170266-56111361-6420
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