Image Restoration In The Presence Of Poisson Noise
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Image Restoration In The Presence Of Poisson Noise
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Author : Mark J. Morisi
language : en
Publisher:
Release Date : 1985
Image Restoration In The Presence Of Poisson Noise written by Mark J. Morisi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with categories.
Image Restoration In The Presence Of Poisson Gaussian Noise
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Author : Anna Maria Jezierska
language : fr
Publisher:
Release Date : 2013
Image Restoration In The Presence Of Poisson Gaussian Noise written by Anna Maria Jezierska and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Cette thèse porte sur la restauration d'images dégradées à la fois par un flou et par un bruit. Une attention particulière est portée aux images issues de la microscopie confocale et notamment celles de macroscopie. Dans ce contexte, un modèle de bruit Poisson-Gauss apparaît bien adapté car il permet de prendre en compte le faible nombre de photons et le fort bruit enregistrés simultanément par les détecteurs. Cependant, ce type de modèle de bruit a été peu exploité car il pose de nombreuses difficultés tant théoriques que pratiques. Dans ce travail, une approche variationnelle est adoptée pour résoudre le problème de restauration dans le cas où le terme de fidélité exact est considéré. La solution du problème peut aussi être interprétée au sens du Maximum A Posteriori (MAP). L'utilisation d'algorithmes primaux-duaux récemment proposés en optimisation convexe permet d'obtenir de bons résultats comparativement à plusieurs approches existantes qui considèrent des approximations variées du terme de fidélité. En ce qui concerne le terme de régularisation de l'approche MAP, des approximations discrète et continue de la pseudo-norme l0 sont considérées. Cette mesure, célèbre pour favoriser la parcimonie, est difficile à optimiser car elle est, à la fois, non convexe et non lisse. Dans un premier temps, une méthode basée sur les coupures de graphes est proposée afin de prendre en compte des à priori de type quadratique tronqué. Dans un second temps, un algorithme à mémoire de gradient de type Majoration-Minimisation, dont la convergence est garantie, est considéré afin de prendre en compte des a priori de type norme l2-l0. Cet algorithme permet notamment d'obtenir de bons résultats dans des problèmes de déconvolution. Néanmoins, un inconvénient des approches variationnelles est qu'elles nécessitent la détermination d'hyperparamètres. C'est pourquoi, deux méthodes, reposant sur une approche Espérance-Maximisation (EM) sont proposées, dans ce travail, afin d'estimer les paramètres d'un bruit Poisson-Gauss: (1) à partir d'une série temporelle d'images (dans ce cas, des paramètres de « bleaching » peuvent aussi être estimés) et (2) à partir d'une seule image. De manière générale, cette thèse propose et teste de nombreuses méthodologies adaptées à la prise en compte de bruits et de flous difficiles, ce qui devrait se révéler utile pour des applications variées, au-delà même de la microscopie.
A Novel Approach To Restoration Of Poissonian Images
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Author : Elad Shaked
language : en
Publisher:
Release Date : 2009
A Novel Approach To Restoration Of Poissonian Images written by Elad Shaked and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the resolution limitations of an imaging device in use and/or by the destructive influence of measurement noise. Specifically, when the noise obeys a Poisson probability law, standard approaches to the problem of image reconstruction are based on using fixed-point algorithms which follow the methodology proposed by Richardson and Lucy in the beginning of the 1970s. The practice of using such methods, however, shows that their convergence properties tend to deteriorate at relatively high noise levels (which typically takes place in so-called low-count settings). This work introduces a novel method for de-noising and/or de-blurring of digital images that have been corrupted by Poisson noise. The proposed method is derived using the framework of MAP estimation, under the assumption that the image of interest can be sparsely represented in the domain of a properly designed linear transform. Consequently, a shrinkage-based iterative procedure is proposed, which guarantees the maximization of an associated maximum-a-posteriori criterion. It is shown in a series of both computer-simulated and real-life experiments that the proposed method outperforms a number of existing alternatives in terms of stability, precision, and computational efficiency.
Dissertation Title
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Author : Md Mujibur Rahman Chowdhury
language : en
Publisher:
Release Date : 2020
Dissertation Title written by Md Mujibur Rahman Chowdhury and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Digital images categories.
Image processing and analysis have gained increasing popularity nowadays for its applications in medical imaging, astronomy, astrophysics, surveillance, image compression, and transmission. In this dissertation, we work on three types of image restoration: image denoising, deconvolution, and computer tomography (CT) reconstruction. In many photonlimited imaging systems, acquired data is usually corrupted by Poisson noise and blurring artifacts. Different from Gaussian noise that is commonly used in the scientific community, Poisson noise depends on the image intensity, which makes image restoration very challenging. Moreover, the underlying images often contain complex geometries, and hence it is desirable to impose a regularization to preserve piecewise smoothness. For this purpose, we propose to use the fractional-order total variation (FOTV) regularization. Specifically, for image denoising, we can establish the existence and uniqueness of a solution to our proposed model. To solve the problem efficiently, we adopt three numerical algorithms based on the Chambolle-Pock primal-dual method, a forward-backward splitting scheme, and the alternating direction method of multipliers (ADMM), each with guaranteed convergence. Various experimental results demonstrate the effectiveness and efficiency of our proposed methods over the state-of-the-art in Poisson denoising. Blurring is always inevitable, as the data recorded by a digital device is an average over neighboring pixels, leading to a blurred image. The blurring process can be modeled as a convolution of an underlying image with a point spread function (PSF). We consider both non-blind and blind image deblurring models, in which blind refers to the case of an unknown PSF. In the pursuit of the high-order smoothness of a restored image, we use the FOTV regularization to remove the blur and Poisson noise simultaneously. We develop an ADMM-based algorithm for non-blind deblurring and an expectation-maximization (EM) algorithm in the blind case. A variety of numerical experiments demonstrate that the proposed algorithms can efficiently reconstruct piecewise-smooth images degraded by Poisson noise and various types of blurring, including Gaussian and motion blurs. Specifically for blind image deblurring, we obtain significant improvements over the state-of-the-art. Lastly, we consider a CT reconstruction problem, where the noise is traditionally modeled by Gaussian distribution. We propose using the FOTV regularization and a data fidelity term for Poisson noise to reconstruct the CT image. We show in experiments that the Gaussian noise is indeed suitable when the highest intensity value (or called peak value) is large. But the Poisson distribution is a more appropriate distribution for relatively lower peak values. Furthermore, we demonstrate that FOTV-based regularization outperforms the classic methods in the CT reconstruction, especially when the data has a small peak value, or the signal-to-noise ratio is low.
Digital Image Restoration
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Author : Aggelos Konstantinos Katsaggelos
language : en
Publisher: Springer
Release Date : 1991
Digital Image Restoration written by Aggelos Konstantinos Katsaggelos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
Recent research results are presented regarding the formulation of the restoration problem as a convex programming problem, the implementation of restoration algorithms using artificial neural networks, the derivation of non-stationary image models and their application to image estimation and restoration, the development of algorithms for the simultaneous image and blur parameter identification and restoration, and the development of algorithms for restoring scanned photographic images.
Image Denoising Of Gaussian And Poisson Noise Based On Wavelet Thresholding
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Author : Jin Quan
language : en
Publisher:
Release Date : 2013
Image Denoising Of Gaussian And Poisson Noise Based On Wavelet Thresholding written by Jin Quan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
Noise on images is generally undesirable and disturbing. It always plays a negative role on higher level processing tasks such as image registration and segmentation. Thus, image denoising becomes a fundamental step necessarily required for better image understanding and interpretation. During the last couple of years, wavelet has been extensively employed in the application of suppressing noise and proven to be a successful tool which outperforms many conventional denoising filters due to its preferred properties. Basically, two generic scenarios occur during the acquisition of images. First, when the detected intensities on the image are sufficiently high, the noise can be suitably modeled as following an additive independent Gaussian distribution. Second, when only a few photons are detected, this observed image is usually modeled as a Poisson process and the intensities to be estimated are assumed to be the underlying Poisson parameters. In this dissertation, these two scenarios are discussed respectively in Part I and Part II. In part I, we consider to reduce the typical additive white Gaussian noise (AWGN). Our driving principle is to decrease the upper bound of the error restricted by the soft-thresholding strategy between the investigated image and noise-free image. Thus we develop a new context modeling method to group coefficients with similar statistics and construct a smoothed version of the noisy image prior to the actual denoising operation. Then, we propose an optimized soft-thresholding denoising function with parameters derived from a modification of a closed form solution which has a more flexible shape and is adaptively pointwise. Furthermore, we extend it to its overcomplete representation by employing the "cycle spinning" method so that the property of shift invariance is achieved which leads to a boost of the denoising performance. By combining these strategies, the denoising results in our experiments confirm that the approach is very competitive to some state-of-the-art denoising methods in terms of quantitative measurements and computational simplicity. In Part II, a new denoising method for Poisson noise corrupted images is proposed which is based on the variance stabilizing transformation (VST) with a new inverse. The VST is used to approximately convert the Poisson noisy image into Gaussian distributed, so that the denoising methods aiming at Gaussian noise can be applied subsequently. The motivation for the improved inverse comes from a main drawback existing in the conventional VSTs such as the Anscombe transformation: its efficiency degrades significantly when the pixel intensities of the observed images are very low due to the biased errors generated by its inverse transformation. In order to correct the biased errors, we introduce a polynomial regression model based on weighted least squares as an alternate to its inverse. Moreover, we incorporate our developed wavelet thresholding strategy for Gaussian noise presented in Part I into the proposed method. We also extend it to the overcomplete representation to suppress the Pseudo-Gibbs phenomena and therefore gains additional denoising effects. Experimental analysis indicates that this method is very competitive.
Optical And Digital Image Processing
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Author : Gabriel Cristobal
language : en
Publisher: John Wiley & Sons
Release Date : 2013-02-12
Optical And Digital Image Processing written by Gabriel Cristobal and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-12 with Technology & Engineering categories.
In recent years, Moore's law has fostered the steady growth of the field of digital image processing, though the computational complexity remains a problem for most of the digital image processing applications. In parallel, the research domain of optical image processing has matured, potentially bypassing the problems digital approaches were suffering and bringing new applications. The advancement of technology calls for applications and knowledge at the intersection of both areas but there is a clear knowledge gap between the digital signal processing and the optical processing communities. This book covers the fundamental basis of the optical and image processing techniques by integrating contributions from both optical and digital research communities to solve current application bottlenecks, and give rise to new applications and solutions. Besides focusing on joint research, it also aims at disseminating the knowledge existing in both domains. Applications covered include image restoration, medical imaging, surveillance, holography, etc... "a very good book that deserves to be on the bookshelf of a serious student or scientist working in these areas." Source: Optics and Photonics News
Image Restoration
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Author : Bahadir Kursat Gunturk
language : en
Publisher: CRC Press
Release Date : 2018-09-03
Image Restoration written by Bahadir Kursat Gunturk and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Computers categories.
Image Restoration: Fundamentals and Advances responds to the need to update most existing references on the subject, many of which were published decades ago. Providing a broad overview of image restoration, this book explores breakthroughs in related algorithm development and their role in supporting real-world applications associated with various scientific and engineering fields. These include astronomical imaging, photo editing, and medical imaging, to name just a few. The book examines how such advances can also lead to novel insights into the fundamental properties of image sources. Addressing the many advances in imaging, computing, and communications technologies, this reference strikes just the right balance of coverage between core fundamental principles and the latest developments in this area. Its content was designed based on the idea that the reproducibility of published works on algorithms makes it easier for researchers to build on each other’s work, which often benefits the vitality of the technical community as a whole. For that reason, this book is as experimentally reproducible as possible. Topics covered include: Image denoising and deblurring Different image restoration methods and recent advances such as nonlocality and sparsity Blind restoration under space-varying blur Super-resolution restoration Learning-based methods Multi-spectral and color image restoration New possibilities using hybrid imaging systems Many existing references are scattered throughout the literature, and there is a significant gap between the cutting edge in image restoration and what we can learn from standard image processing textbooks. To fill that need but avoid a rehash of the many fine existing books on this subject, this reference focuses on algorithms rather than theories or applications. Giving readers access to a large amount of downloadable source code, the book illustrates fundamental techniques, key ideas developed over the years, and the state of the art in image restoration. It is a valuable resource for readers at all levels of understanding.
Semi Blind Image Deblurring In The Presence Of Poisson Photon Noise Via Mumford Shah Regularization
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Author : Chanan Gazala
language : en
Publisher:
Release Date : 2007
Semi Blind Image Deblurring In The Presence Of Poisson Photon Noise Via Mumford Shah Regularization written by Chanan Gazala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Image processing categories.
Image Restoration
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Author : Aymeric Histace
language : en
Publisher: BoD – Books on Demand
Release Date : 2012-04-04
Image Restoration written by Aymeric Histace and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-04 with Computers categories.
This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with.