106 research outputs found
A multiresolution framework for local similarity based image denoising
In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise
Ancient Documents Denoising and Decomposition Using Aujol and Chambolle Algorithm
With the improvement of printing technology since the 15th century, there is a huge amount of printed documents published and distributed. These documents are degraded by the time and require to be preprocessed before being submitted to image indexing strategy, in order to enhance the quality of images. This paper proposes a new pre-processing that permits to denoise these documents, by using a Aujol and Chambolle algorithm. Aujol and Chambolle algorithm allows to extract meaningful components from image. In this case, we can extract shapes, textures and noise. Some examples of specific processings applied on each layer are illustrated in this paper
On the application of partial differential equations and fractional partial differential equations to images and their methods of solution
This body of work examines the plausibility of applying partial di erential equations and
time-fractional partial di erential equations to images. The standard di usion equation
is coupled with a nonlinear cubic source term of the Fitzhugh-Nagumo type to obtain a
model with di usive properties and a binarizing e ect due to the source term. We examine
the e ects of applying this model to a class of images known as document images;
images that largely comprise text. The e ects of this model result in a binarization process
that is competitive with the state-of-the-art techniques. Further to this application,
we provide a stability analysis of the method as well as high-performance implementation
on general purpose graphical processing units. The model is extended to include
time derivatives to a fractional order which a ords us another degree of control over this
process and the nature of the fractionality is discussed indicating the change in dynamics
brought about by this generalization. We apply a semi-discrete method derived by
hybridizing the Laplace transform and two discretization methods: nite-di erences and
Chebyshev collocation. These hybrid techniques are coupled with a quasi-linearization
process to allow for the application of the Laplace transform, a linear operator, to a
nonlinear equation of fractional order in the temporal domain. A thorough analysis
of these methods is provided giving rise to conditions for solvability. The merits and
demerits of the methods are discussed indicating the appropriateness of each method
Advanced Image Acquisition, Processing Techniques and Applications
"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
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