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19 relations: Anisotropic diffusion, Bregman method, Compressed sensing, Convex function, Convex optimization, Differentiable function, Digital image processing, Digital signal (signal processing), Euler–Lagrange equation, Gaussian blur, Gradient, Interior-point method, Isotropy, Median filter, Noise reduction, Non-local means, Regularization (mathematics), Signal processing, Total variation.
Anisotropic diffusion
In image processing and computer vision, anisotropic diffusion, also called Perona–Malik diffusion, is a technique aiming at reducing image noise without removing significant parts of the image content, typically edges, lines or other details that are important for the interpretation of the image.
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Bregman method
Bregman's method is an iterative algorithm to solve certain convex optimization problems.
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Compressed sensing
Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems.
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Convex function
In mathematics, a real-valued function defined on an ''n''-dimensional interval is called convex (or convex downward or concave upward) if the line segment between any two points on the graph of the function lies above or on the graph, in a Euclidean space (or more generally a vector space) of at least two dimensions.
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Convex optimization
Convex optimization is a subfield of optimization that studies the problem of minimizing convex functions over convex sets.
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Differentiable function
In calculus (a branch of mathematics), a differentiable function of one real variable is a function whose derivative exists at each point in its domain.
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Digital image processing
In computer science, Digital image processing is the use of computer algorithms to perform image processing on digital images.
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Digital signal (signal processing)
In the context of digital signal processing (DSP), a digital signal is a discrete-time signal for which not only the time but also the amplitude has discrete values; in other words, its samples take on only values from a discrete set (a countable set that can be mapped one-to-one to a subset of integers).
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Euler–Lagrange equation
In the calculus of variations, the Euler–Lagrange equation, Euler's equation, or Lagrange's equation (although the latter name is ambiguous—see disambiguation page), is a second-order partial differential equation whose solutions are the functions for which a given functional is stationary.
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Gaussian blur
In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss).
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Gradient
In mathematics, the gradient is a multi-variable generalization of the derivative.
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Interior-point method
Interior-point methods (also referred to as barrier methods) are a certain class of algorithms that solve linear and nonlinear convex optimization problems.
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Isotropy
Isotropy is uniformity in all orientations; it is derived from the Greek isos (ἴσος, "equal") and tropos (τρόπος, "way").
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Median filter
The median filter is a nonlinear digital filtering technique, often used to remove noise from an image or signal.
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Noise reduction
Noise reduction is the process of removing noise from a signal.
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Non-local means
Non-local means is an algorithm in image processing for image denoising.
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Regularization (mathematics)
In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.
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Signal processing
Signal processing concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying "information about the behavior or attributes of some phenomenon", such as sound, images, and biological measurements.
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Total variation
In mathematics, the total variation identifies several slightly different concepts, related to the (local or global) structure of the codomain of a function or a measure.
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