图像处理matlab版源码V1.1.3(第四章)

由天下分享时间：2024/12/27 8:53:06 加入收藏我要投稿点赞

1、histroi

function [p, npix] = histroi(f, c, r)

%HISTROI Computes the histogram of an ROI in an image.

% [P, NPIX] = HISTROI(F, C, R) computes the histogram, P, of a % polygonal region of interest (ROI) in image F. The polygonal % region is defined by the column and row coordinates of its

% vertices, which are specified (sequentially) in vectors C and R, % respectively. All pixels of F must be >= 0. Parameter NPIX is the % number of pixels in the polygonal region.

% Generate the binary mask image. B = roipoly(f, c, r);

% Compute the histogram of the pixels in the ROI. p = imhist(f(B));

% Obtain the number of pixels in the ROI if requested in the output. if nargout > 1

npix = sum(B(:)); end

2、imnoise2

function R = imnoise2(type, M, N, a, b)

%IMNOISE2 Generates an array of random numbers with specified PDF. % R = IMNOISE2(TYPE, M, N, A, B) generates an array, R, of size % M-by-N, whose elements are random numbers of the specified TYPE % with parameters A and B. If only TYPE is included in the

% input argument list, a single random number of the specified % TYPE and default parameters shown below is generated. If only % TYPE, M, and N are provided, the default parameters shown below % are used. If M = N = 1, IMNOISE2 generates a single random % number of the specified TYPE and parameters A and B. %

% Valid values for TYPE and parameters A and B are: %

% 'uniform' Uniform random numbers in the interval (A, B). % The default values are (0, 1).

% 'gaussian' Gaussian random numbers with mean A and standard % deviation B. The default values are A = 0, B = 1. % 'salt & pepper' Salt and pepper numbers of amplitude 0 with % probability Pa = A, and amplitude 1 with

% probability Pb = B. The default values are Pa = % Pb = A = B = 0.05. Note that the noise has

% values 0 (with probability Pa = A) and 1 (with % probability Pb = B), so scaling is necessary if % values other than 0 and 1 are required. The noise % matrix R is assigned three values. If R(x, y) = % 0, the noise at (x, y) is pepper (black). If % R(x, y) = 1, the noise at (x, y) is salt % (white). If R(x, y) = 0.5, there is no noise % assigned to coordinates (x, y).

% 'lognormal' Lognormal numbers with offset A and shape % parameter B. The defaults are A = 1 and B = % 0.25.

% 'rayleigh' Rayleigh noise with parameters A and B. The % default values are A = 0 and B = 1.

% 'exponential' Exponential random numbers with parameter A. The % default is A = 1.

% 'erlang' Erlang (gamma) random numbers with parameters A % and B. B must be a positive integer. The % defaults are A = 2 and B = 5. Erlang random % numbers are approximated as the sum of B % exponential random numbers.

% Set default values. if nargin == 1 a = 0; b = 1; M = 1; N = 1; elseif nargin == 3 a = 0; b = 1; end

% Begin processing. Use lower(type) to protect against input being % capitalized. switch lower(type) case 'uniform'

R = a + (b - a)*rand(M, N); case 'gaussian'

R = a + b*randn(M, N); case 'salt & pepper' if nargin <= 3

a = 0.05; b = 0.05; end

% Check to make sure that Pa + Pb is not > 1. if (a + b) > 1

error('The sum Pa + Pb must not exceed 1.')

end

R(1:M, 1:N) = 0.5;

% Generate an M-by-N array of uniformly-distributed random numbers % in the range (0, 1). Then, Pa*(M*N) of them will have values <= % a. The coordinates of these points we call 0 (pepper

% noise). Similarly, Pb*(M*N) points will have values in the range % > a & <= (a + b). These we call 1 (salt noise). X = rand(M, N); c = find(X <= a); R(c) = 0; u = a + b;

c = find(X > a & X <= u); R(c) = 1; case 'lognormal' if nargin <= 3

a = 1; b = 0.25; end

R = a*exp(b*randn(M, N)); case 'rayleigh'

R = a + (-b*log(1 - rand(M, N))).^0.5; case 'exponential' if nargin <= 3 a = 1; end

if a <= 0

error('Parameter a must be positive for exponential type.') end

k = -1/a;

R = k*log(1 - rand(M, N)); case 'erlang' if nargin <= 3 a = 2; b = 5; end

if (b ~= round(b) | b <= 0)

error('Param b must be a positive integer for Erlang.') end

k = -1/a;

R = zeros(M, N); for j = 1:b