Titlebook: Denoising of Photographic Images and Video; Fundamentals, Open C Marcelo Bertalmío Book 2018 Springer Nature Switzerland AG 2018 Image Proc

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Gaussian Priors for Image Denoising,r image restoration. In a Bayesian framework, such priors on patches can be used for instance to estimate a clean patch from its noisy version, via classical estimators such as the conditional expectation or the maximum a posteriori. As we will recall, in the case of Gaussian white noise, simply ass

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,Internal Versus External Denoising—Benefits and Bounds, denoising approaches, such as BM3D, utilize spatial redundancy of patches (relatively small, cropped windows) either within a single natural image, or within a large collection of natural images. In this chapter, we summarize our previous finding that “Internal-Denoising” (based on internal noisy p

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Patch-Based Methods for Video Denoising,till image denoising algorithms; however, it is possible to take advantage of the redundant information contained in the sequence to improve the denoising results. Most recent algorithms are patch based. These methods have two clearly differentiated steps: select similar patches to a reference one a

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Image and Video Noise: An Industry Perspective,l applications of imagery. In this chapter, we will examine the problem of image noise from an industrial and commercial viewpoint. We will consider how noise enters the imaging chain in these settings and how noise is measured and quantified for later removal. We will also discuss standards and sta

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Modeling and Estimation of Signal-Dependent and Correlated Noise,essential mathematical setting for the observed signals. The distribution families covered as leading examples include Poisson, mixed Poisson–Gaussian, various forms of signal-dependent Gaussian noise (including multiplicative families and approximations of the Poisson family), as well as doubly cen

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Sparsity-Based Denoising of Photographic Images: From Model-Based to Data-Driven,sidue learning). The overarching theme of our review is to provide a unified conceptual understanding of why and how sparsity-based image denoising works—in particular, the evolving role played by . and .. Based on our critical review, we will discuss a few open issues and promising directions for f

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