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Category-Specific Object Image Denoising

Anwar, Saeed; Porikli, Fatih; Huynh, Cong

Description

We present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms that search patches either from a generic database or noisy image itself, our method first selects clean images similar to the noisy image from a database that consists of images of the same class. Then, within the spatial locality of each noisy patch, it assembles a set of “support patches” from the selected images. These noisy-free...[Show more]

dc.contributor.authorAnwar, Saeed
dc.contributor.authorPorikli, Fatih
dc.contributor.authorHuynh, Cong
dc.date.accessioned2020-12-20T20:51:50Z
dc.date.available2020-12-20T20:51:50Z
dc.identifier.issn1057-7149
dc.identifier.urihttp://hdl.handle.net/1885/217897
dc.description.abstractWe present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms that search patches either from a generic database or noisy image itself, our method first selects clean images similar to the noisy image from a database that consists of images of the same class. Then, within the spatial locality of each noisy patch, it assembles a set of “support patches” from the selected images. These noisy-free support samples resemble the noisy patch and correspond principally to the identical part of the depicted object. In addition, we employ a content adaptive distribution model for each patch, where we derive the parameters of the distribution from the support patches. We formulate noise removal task as an optimization problem in the transform domain. Our objective function composed of a Gaussian fidelity term that imposes category specific information, and a low-rank term that encourages the similarity between the noisy and the support patches in a robust manner. The denoising process is driven by an iterative selection of support patches and optimization of the objective function. Our extensive experiments on five different object categories confirm the benefit of incorporating category-specific information to noise removal and demonstrate the superior performance of our method over the state-of-the-art alternatives.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.rights© 2017 IEEE
dc.sourceIEEE Transactions on Image Processing
dc.titleCategory-Specific Object Image Denoising
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume26
dc.date.issued2017-07-13
local.identifier.absfor091599 - Interdisciplinary Engineering not elsewhere classified
local.identifier.ariespublicationu4351680xPUB55
local.type.statusPublished Version
local.contributor.affiliationAnwar, Saeed, College of Engineering and Computer Science, ANU
local.contributor.affiliationPorikli, Fatih, College of Engineering and Computer Science, ANU
local.contributor.affiliationHuynh, Cong, College of Engineering and Computer Science, ANU
local.description.embargo2099-12-31
local.bibliographicCitation.issue11
local.bibliographicCitation.startpage5506
local.bibliographicCitation.lastpage5518
local.identifier.doi10.1109/TIP.2017.2733739
dc.date.updated2020-11-23T10:17:49Z
local.identifier.scopusID2-s2.0-85028846383
local.identifier.thomsonID000408817400010
CollectionsANU Research Publications

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