Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/124424
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dc.date.accessioned2017-01-04T11:05:06Z-
dc.date.available2017-01-04T11:05:06Z-
dc.identifier.urihttp://hdl.handle.net/10603/124424-
dc.description.abstractImage denoising has always been one of the standard problems of the image analysis and processing community. It is motivated by itself or by some practical application such as preprocessing for e.g. remote sensing applications, medical image diagnosis; the goal is to reduce noise. The successful image denoising algorithms are mainly based on transforms. Recent research in transform based image denoising has stressed on the wavelet transform due to its superior performance over other transforms such as Fourier, Karhunen-Loeve transform, Discrete cosine transform. It is applied to image processing successfully for last two decades. It has been shown in several papers that wavelet-based methods arise naturally for image denoising. newlineThe first part of this thesis is for additive noise. The proposed algorithms use local adaptivity based on static of neighboring pixels in wavelet domain. These statistics consist of energy and variance in different scales that capture certain statistical regularities of natural images. The existing methods are limited because they make at least one of the following three assumptions: i) the wavelet coefficients are independent; ii) the signal component of the wavelet coefficient distribution follows a specified parametric model; and iii) the wavelet representation of all signal of interest has same level of sparsity. We propose two methods namely locally adaptive energy (LAE) and locally adaptive variance (LAV), based on locally adaptivity that addresses each of these issues. The algorithm uses Discrete Wavelet Transform (DWT) to extract information about sharp features in multiresolution images and applies shrinkage function adapting the local statistics of the image. The shrinkage function depends on standard deviation or newlineiii newlineenergy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. The algorithms use very few and intuitive parameter. The parameters are first chosen by heuristi
dc.format.extentxix
dc.languageEnglish
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dc.rightsuniversity
dc.titleWavelet based image denoising techniques for additive and multiplicative noises
dc.title.alternative
dc.creator.researcherGupta, Karunesh Kumar
dc.subject.keywordimage denoising, additive and multiplicative noises
dc.description.note
dc.contributor.guideGupta, Rajiv
dc.publisher.placePilani
dc.publisher.universityBirla Institute of Technology and Science
dc.publisher.institutionElectrical and Electronics Engineering
dc.date.registered1/7/2000
dc.date.completed
dc.date.awarded1/8/2007
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dc.format.accompanyingmaterialCD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Electrical & Electronics Engineering

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