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Journal ArticleDOI

The digital TV filter and nonlinear denoising

TLDR
The digital TV filter is a data dependent lowpass filter, capable of denoising data without blurring jumps or edges, which solves a global total variational (or L(1)) optimization problem, which differs from most statistical filters.
Abstract
Motivated by the classical TV (total variation) restoration model, we propose a new nonlinear filter-the digital TV filter for denoising and enhancing digital images, or more generally, data living on graphs. The digital TV filter is a data dependent lowpass filter, capable of denoising data without blurring jumps or edges. In iterations, it solves a global total variational (or L/sup 1/) optimization problem, which differs from most statistical filters. Applications are given in the denoising of one dimensional (1-D) signals, two-dimensional (2-D) data with irregular structures, gray scale and color images, and nonflat image features such as chromaticity.

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Citations
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Book

Computer Vision: Algorithms and Applications

TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
Journal ArticleDOI

Fast and robust multiframe super resolution

TL;DR: This paper proposes an alternate approach using L/sub 1/ norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models and demonstrates its superiority to other super-resolution methods.

Single Image Haze Removal Using Dark Channel Prior

TL;DR: This thesis develops an effective but very simple prior, called the dark channel prior, to remove haze from a single image, and thus solves the ambiguity of the problem.
Journal ArticleDOI

Kernel Regression for Image Processing and Reconstruction

TL;DR: This paper adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more and establishes key relationships with some popular existing methods and shows how several of these algorithms are special cases of the proposed framework.
Journal ArticleDOI

Nonlocal Operators with Applications to Image Processing

TL;DR: This topic can be viewed as an extension of spectral graph theory and the diffusion geometry framework to functional analysis and PDE-like evolutions to define new types of flows and functionals for image processing and elsewhere.
References
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Journal ArticleDOI

Nonlinear total variation based noise removal algorithms

TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI

Scale-space and edge detection using anisotropic diffusion

TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Journal ArticleDOI

De-noising by soft-thresholding

TL;DR: The authors prove two results about this type of estimator that are unprecedented in several ways: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures.
Book

Anisotropic diffusion in image processing

TL;DR: This work states that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition, which means that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution.
Journal ArticleDOI

Image recovery via total variation minimization and related problems

TL;DR: A variant of the original TV minimization problem that handles correctly some situations where TV fails is proposed, and an alternative approach whose purpose is to handle the minimization of the minimum of several convex functionals is proposed.
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