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Linear and Nonlinear Filters—Wolfram Language Documentation

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• Functions
  • BandpassFilter
  • BandstopFilter
  • BilateralFilter
  • BoxMatrix
  • CellularAutomaton
  • CornerFilter
  • CrossMatrix
  • DerivativeFilter
  • DiamondMatrix
  • DifferentiatorFilter
  • DiskMatrix
  • EntropyFilter
  • GaussianFilter
  • GaussianMatrix
  • GradientFilter
  • HighpassFilter
  • HilbertFilter
  • IdentityMatrix
  • KuwaharaFilter
  • LaplacianFilter
  • ListConvolve
  • ListCorrelate
  • ListDeconvolve
  • LowpassFilter
  • MaxFilter
  • MeanFilter
  • MeanShiftFilter
  • MedianFilter
  • MinFilter
  • MovingAverage
  • RidgeFilter
  • SparseArray
  • TotalVariationFilter
  • WienerFilter
• Related Guides
  • Data Transforms and Smoothing
  • Image Neighborhood Processing
  • Signal Processing
  • Feature Detection
  • Wavelet Analysis
  • Time Series Analysis

The Wolfram Language's highly optimized filtering capabilities provide a wide range of linear and modern nonlinear local filters, as well as a variety of nonlocal filters, which can be applied to arbitrary arrays of data and images.

GaussianFilter — Gaussian and Gaussian derivatives filtering of images and arrays

DerivativeFilter — general-order derivative filter

MeanFilter  ▪  GradientFilter  ▪  LaplacianFilter  ▪  WienerFilter  ▪  MovingAverage

LowpassFilter  ▪  HighpassFilter  ▪  BandpassFilter  ▪  BandstopFilter  ▪  HilbertFilter  ▪  DifferentiatorFilter

ListConvolve, ListCorrelate — convolve, correlate with any kernel

DiskMatrix  ▪  BoxMatrix  ▪  DiamondMatrix  ▪  CrossMatrix

GaussianMatrix  ▪  IdentityMatrix  ▪  SparseArray

MedianFilter  ▪  MinFilter  ▪  MaxFilter  ▪  MeanShiftFilter  ▪  EntropyFilter  ▪  CornerFilter  ▪  RidgeFilter  ▪  KuwaharaFilter  ▪  BilateralFilter

CellularAutomaton — general cellular automaton

TotalVariationFilter  ▪  ListDeconvolve

▪
• Data Transforms and Smoothing
▪
• Image Neighborhood Processing
▪
• Signal Processing
▪
• Feature Detection
▪
• Wavelet Analysis
▪
• Time Series Analysis

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