imnoise.m

来自「A toolbox for the non-local means algori」· M 代码 · 共 65 行

function b = imnoise(varargin)
%IMNOISE Add noise to image.
%   J = IMNOISE(I,TYPE,...) Add noise of a given TYPE to the intensity image
%   I. TYPE is a string that can have one of these values:
%
%       'gaussian'       Gaussian white noise with constant
%                        mean and variance
%
%       'localvar'       Zero-mean Gaussian white noise 
%                        with an intensity-dependent variance
%
%       'poisson'        Poisson noise
%
%       'salt & pepper'  "On and Off" pixels
%
%       'speckle'        Multiplicative noise
%
%   Depending on TYPE, you can specify additional parameters to IMNOISE. All
%   numerical parameters are normalized; they correspond to operations with
%   images with intensities ranging from 0 to 1.
%   
%   J = IMNOISE(I,'gaussian',M,V) adds Gaussian white noise of mean M and
%   variance V to the image I. When unspecified, M and V default to 0 and
%   0.01 respectively.
%   
%   J = imnoise(I,'localvar',V) adds zero-mean, Gaussian white noise of
%   local variance, V, to the image I.  V is an array of the same size as I.
%
%   J = imnoise(I,'localvar',IMAGE_INTENSITY,VAR) adds zero-mean, Gaussian
%   noise to an image, I, where the local variance of the noise is a
%   function of the image intensity values in I.  IMAGE_INTENSITY and VAR
%   are vectors of the same size, and PLOT(IMAGE_INTENSITY,VAR) plots the
%   functional relationship between noise variance and image intensity.
%   IMAGE_INTENSITY must contain normalized intensity values ranging from 0
%   to 1.
%
%   J = IMNOISE(I,'poisson') generates Poisson noise from the data instead
%   of adding artificial noise to the data. In order to respect Poisson
%   statistics, the intensities of uint8 and uint16 images must correspond
%   to the number of photons (or any other quanta of information).
%   Double-precision images are used when the number of photons per pixel
%   can be much larger than 65535 (but less than 10^12); the intensities
%   values vary between 0 and 1 and correspond to the number of photons
%   divided by 10^12.
%
%   J = IMNOISE(I,'salt & pepper',D) adds "salt and pepper" noise to the
%   image I, where D is the noise density.  This affects approximately
%   D*numel(I) pixels. The default for D is 0.05.
%   
%   J = IMNOISE(I,'speckle',V) adds multiplicative noise to the image I,
%   using the equation J = I + n*I, where n is uniformly distributed random
%   noise with mean 0 and variance V. The default for V is 0.04.
%
%   Note
%   ----
%   The mean and variance parameters for 'gaussian', 'localvar', and
%   'speckle' noise types are always specified as if for a double image
%   in the range [0, 1].  If the input image is of class uint8 or uint16,
%   the imnoise function converts the image to double, adds noise
%   according to the specified type and parameters, and then converts the
%   noisy image back to the same class as the input.
%   
%   Class Support
%   -------------
%   I can be of class uint8, uint16, or double.