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1.matlab 没有wavefast函数,源码怎么回事,我是2012a
2.Matlab实现小波变换

matlab 没有wavefast函数,怎么回事,我是2012a

       function [c, s] = wavefast(x, n, varargin)

       %WAVEFAST Perform multi-level 2-dimensional fast wavelet transform.

       % [C, L] = WAVEFAST(X, N, LP, HP) performs a 2D N-level FWT of

       % image (or matrix) X with respect to decomposition filters LP and

       % HP.

       %

       % [C, L] = WAVEFAST(X, N, WNAME) performs the same operation but

       % fetches filters LP and HP for wavelet WNAME using WAVEFILTER.

       %

       % Scale parameter N must be less than or equal to log2 of the

       % maximum image dimension. Filters LP and HP must be even. To

       % reduce border distortion, X is symmetrically extended. That is,

       % if X = [c1 c2 c3 ... cn] (in 1D), then its symmetric extension

       % would be [... c3 c2 c1 c1 c2 c3 ... cn cn cn-1 cn-2 ...].

       %

       % OUTPUTS:

       % Matrix C is a coefficient decomposition vector:

       %

       % C = [ a(n) h(n) v(n) d(n) h(n-1) ... v(1) d(1) ]

       %

       % where a, h, v, and d are columnwise vectors containing

       % approximation, horizontal, vertical, and diagonal coefficient

       % matrices, respectively. C has 3n + 1 sections where n is the

       % number of wavelet decompositions.

       %

       % Matrix S is an (n+2) x 2 bookkeeping matrix:

       %

       % S = [ sa(n, :); sd(n, :); sd(n-1, :); ... ; sd(1, :); sx ]

       %

       % where sa and sd are approximation and detail size entries.

       %

       % See also WAVEBACK and WAVEFILTER.

       % Copyright - R. C. Gonzalez, R. E. Woods, & S. L. Eddins

       % Digital Image Processing Using MATLAB, Prentice-Hall,

       % $Revision: 1.5 $ $Date: // :: $

       % Check the input arguments for reasonableness.

       error(nargchk(3, 4, nargin));

       if nargin == 3

        if ischar(varargin{ 1})

        [lp, hp] = wavefilter(varargin{ 1}, 'd');

        else

        error('Missing wavelet name.');

        end

       else

        lp = varargin{ 1}; hp = varargin{ 2};

       end

       fl = length(lp); sx = size(x);

       if (ndims(x) ~= 2) | (min(sx) < 2) | ~isreal(x) | ~isnumeric(x)

        error('X must be a real, numeric matrix.');

       end

       if (ndims(lp) ~= 2) | ~isreal(lp) | ~isnumeric(lp) ...

        | (ndims(hp) ~= 2) | ~isreal(hp) | ~isnumeric(hp) ...

        | (fl ~= length(hp)) | rem(fl, 2) ~= 0

        error(['LP and HP must be even and equal length real, ' ...

        'numeric filter vectors.']);

       end

       if ~isreal(n) | ~isnumeric(n) | (n < 1) | (n > log2(max(sx)))

        error(['N must be a real scalar between 1 and ' ...

        'log2(max(size((X))).']);

       end

       % Init the starting output data structures and initial approximation.

       c = []; s = sx; app = double(x);

       % For each decomposition ...

       for i = 1:n

        % Extend the approximation symmetrically.

        [app, keep] = symextend(app, fl);

        % Convolve rows with HP and downsample. Then convolve columns

        % with HP and LP to get the diagonal and vertical coefficients.

        rows = symconv(app, hp, 'row', fl, keep);

        coefs = symconv(rows, hp, 'col', fl, keep);

        c = [coefs(:)' c]; s = [size(coefs); s];

        coefs = symconv(rows, lp, 'col', fl, keep);

        c = [coefs(:)' c];

        % Convolve rows with LP and downsample. Then convolve columns

        % with HP and LP to get the horizontal and next approximation

        % coeffcients.

        rows = symconv(app, lp, 'row', fl, keep);

        coefs = symconv(rows, hp, 'col', fl, keep);

        c = [coefs(:)' c];

        app = symconv(rows, lp, 'col', fl, keep);

       end

       % Append final approximation structures.

       c = [app(:)' c]; s = [size(app); s];

       %-------------------------------------------------------------------%

       function [y, keep] = symextend(x, fl)

       % Compute the number of coefficients to keep after convolution

       % and downsampling. Then extend x in both dimensions.

       keep = floor((fl + size(x) - 1) / 2);

       y = padarray(x, [(fl - 1) (fl - 1)], 'symmetric', 'both');

       %-------------------------------------------------------------------%

       function y = symconv(x, h, type, fl, keep)

       % Convolve the rows or columns of x with h, downsample,

       % and extract the center section since symmetrically extended.

       if strcmp(type, 'row')

        y = conv2(x, h);

        y = y(:, 1:2:end);

        y = y(:, fl / 2 + 1:fl / 2 + keep(2));

       else

        y = conv2(x, h');

        y = y(1:2:end, :);

        y = y(fl / 2 + 1:fl / 2 + keep(1), :);

       end

Matlab实现小波变换

       实现小波变换的MATLAB操作涉及到一系列关键步骤与函数，包括使用Haar滤波器进行简单FWT（离散小波变换），源码比较函数wavefast和wavedec2的源码执行时间，以及探索小波的源码linux小火车源码方向性和边缘检测能力。

       首先，源码使用Haar滤波器作为基本工具，源码腾讯德州扑克源码MATLAB中的源码小波函数提供了一种简便的执行方法。通过调用这些函数，源码用户可以对图像或信号进行离散小波变换，源码实现数据的源码多分辨率分析。

       对比函数wavefast和wavedec2的源码性能表现，用户可利用MATLAB的源码内置功能评估不同算法的效率和精确度。这种比较有助于在实际应用中选择最适合特定任务的源码linux系统看源码算法。

       探索小波的源码方向性，是源码深入理解其在边缘检测等应用中优势的关键。MATLAB提供了丰富的qq闪图源码工具和函数，如小波包分析等，用户可以使用这些工具来分析和处理不同方向的边缘信息，从而提高图像处理的qq开通会员源码精度和效果。

       参考文献为了解小波变换的理论基础与应用提供了重要资源，包括Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins的《Digital Image Processing Using MATLAB》、阮秋琦的《数字图像处理（MATLAB版）》、以及冈萨雷斯的《数字图像处理（第三版）》。这些文献不仅提供了理论知识，还包含了许多实用的MATLAB代码示例。

       为了获取文章和代码等相关资源，用户可以访问Github仓库： digital-image-processing-matlab，或者通过公众号AIShareLab回复“数字图像处理”获取资源。