冈萨雷斯数字图像处理——彩色图像增强3实例

川长思鸟来 2022-08-18 15:18 166阅读 0赞

# **实例一：基于直方图均衡化的彩色图像增强** #

要处理的图片：fruit.jpg

![Center][]

example1.m:

clc;
    RGB=imread('fruit.jpg');    %输入彩色图像，得到三维数组
    
    R=RGB(:,:,1);             %分别取三维数组的一维，得到红绿蓝三个分量
    G=RGB(:,:,2);             %为R G B
    B=RGB(:,:,3);
    
    subplot(4,2,1),imshow(RGB);       %绘制各分量的图像及其直方图
    title('原始真彩色图像');          
    subplot(4,2,3),imshow(R);
    title('真彩色图像的红色分量');
    subplot(4,2,4), imhist(R);
    title('真彩色图像的红色分量直方图');
    subplot(4,2,5),imshow(G);
    title('真彩色图像的绿色分量');
    subplot(4,2,6), imhist(G);
    title('真彩色图像的绿色分量直方图');
    subplot(4,2,7),imshow(B);
    title('真彩色图像的蓝色分量');
    subplot(4,2,8), imhist(B);
    title('真彩色图像的蓝色分量直方图');
    
    r=histeq(R);             %对个分量直方图均衡化，得到个分量均衡化图像   
    g=histeq(G);  
    b=histeq(B); 
    figure,
    subplot(3,2,1),imshow(r);
    title('红色分量均衡化后图像');
    subplot(3,2,2), imhist(r);
    title('红色分量均衡化后图像直方图');
    subplot(3,2,3),imshow(g);
    title('绿色分量均衡化后图像');
    subplot(3,2,4), imhist(g);
    title('绿色分量均衡化后图像直方图');
    subplot(3,2,5), imshow(b);
    title('蓝色分量均衡化后图像');
    subplot(3,2,6), imhist(b);
    title('蓝色分量均衡化后图像直方图');
    
    figure,       %通过均衡化后的图像还原输出原图像                    
    newimg = cat(3,r,g,b);                
    imshow(newimg,[]);
    title('均衡化后分量图像还原输出原图');

处理效果：

![SouthEast][]

![SouthEast 1][]

![SouthEast 2][]

通过matlab仿真，比较均衡化后的还原图像与输入原始真彩色图像，输出图像轮廓更清晰，亮度明显增强。

# 实例二: 双边滤波(Bilateral Filtering) #

双边滤波是一种非线性的滤波方法，是结合图像的空间邻近度和像素值相似度的一种折衷处理，同时考虑空域信息和灰度相似性，达到保边去噪的目的。具有简单、非迭代、局部的特点。  
 双边滤波器的好处是可以做边缘保存（edge preserving），一般过去用的维纳滤波或者高斯滤波去降噪，都会较明显地模糊边缘，对于高频细节的保护效果并不明显。双边滤波器顾名思义比 [高斯滤波][Link 1] 多了一个高斯方差sigma－d，它是基于空间分布的高斯滤波 [函数][Link 2] ，所以在边缘附近，离的较远的像素不会太多影响到边缘上的像素值，这样就保证了边缘附近像素值的保存。但是由于保存了过多的高频信息，对于彩色图像里的高频噪声，双边滤波器不能够干净的滤掉，只能够对于低频信息进行较好的滤波。

测试用例图：

![SouthEast 3][]

matlab代码：

bfilter2.m

% BFILTER2 Two dimensional bilateral filtering.
    %    This function implements 2-D bilateral filtering using
    %    the method outlined in:
    %
    %       C. Tomasi and R. Manduchi. Bilateral Filtering for 
    %       Gray and Color Images. In Proceedings of the IEEE 
    %       International Conference on Computer Vision, 1998. 
    %
    %    B = bfilter2(A,W,SIGMA) performs 2-D bilateral filtering
    %    for the grayscale or color image A. A should be a double
    %    precision matrix of size NxMx1 or NxMx3 (i.e., grayscale
    %    or color images, respectively) with normalized values in
    %    the closed interval [0,1]. The half-size of the Gaussian
    %    bilateral filter window is defined by W. The standard
    %    deviations of the bilateral filter are given by SIGMA,
    %    where the spatial-domain standard deviation is given by
    %    SIGMA(1) and the intensity-domain standard deviation is
    %    given by SIGMA(2).
    %
    % Douglas R. Lanman, Brown University, September 2006.
    % dlanman@brown.edu, http://mesh.brown.edu/dlanman
    
    
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Pre-process input and select appropriate filter.
    function B = bfilter2(A,w,sigma)
    
    % Verify that the input image exists and is valid.
    if ~exist('A','var') || isempty(A)
       error('Input image A is undefined or invalid.');
    end
    if ~isfloat(A) || ~sum([1,3] == size(A,3)) || ...
          min(A(:)) < 0 || max(A(:)) > 1
       error(['Input image A must be a double precision ',...
              'matrix of size NxMx1 or NxMx3 on the closed ',...
              'interval [0,1].']);      
    end
    
    % Verify bilateral filter window size.
    if ~exist('w','var') || isempty(w) || ...
          numel(w) ~= 1 || w < 1
       w = 5;
    end
    w = ceil(w);
    
    % Verify bilateral filter standard deviations.
    if ~exist('sigma','var') || isempty(sigma) || ...
          numel(sigma) ~= 2 || sigma(1) <= 0 || sigma(2) <= 0
       sigma = [3 0.1];
    end
    
    % Apply either grayscale or color bilateral filtering.
    if size(A,3) == 1
       B = bfltGray(A,w,sigma(1),sigma(2));
    else
       B = bfltColor(A,w,sigma(1),sigma(2));
    end
    
    
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Implements bilateral filtering for grayscale images.
    function B = bfltGray(A,w,sigma_d,sigma_r)
    
    % Pre-compute Gaussian distance weights.
    [X,Y] = meshgrid(-w:w,-w:w);
    G = exp(-(X.^2+Y.^2)/(2*sigma_d^2));
    
    % Create waitbar.
    h = waitbar(0,'Applying bilateral filter...');
    set(h,'Name','Bilateral Filter Progress');
    
    % Apply bilateral filter.
    dim = size(A);
    B = zeros(dim);
    for i = 1:dim(1)
       for j = 1:dim(2)
          
             % Extract local region.
             iMin = max(i-w,1);
             iMax = min(i+w,dim(1));
             jMin = max(j-w,1);
             jMax = min(j+w,dim(2));
             I = A(iMin:iMax,jMin:jMax);
          
             % Compute Gaussian intensity weights.
             H = exp(-(I-A(i,j)).^2/(2*sigma_r^2));
          
             % Calculate bilateral filter response.
             F = H.*G((iMin:iMax)-i+w+1,(jMin:jMax)-j+w+1);
             B(i,j) = sum(F(:).*I(:))/sum(F(:));
                   
       end
       waitbar(i/dim(1));
    end
    
    % Close waitbar.
    close(h);
    
    
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Implements bilateral filter for color images.
    function B = bfltColor(A,w,sigma_d,sigma_r)
    
    % Convert input sRGB image to CIELab color space.
    if exist('applycform','file')
       A = applycform(A,makecform('srgb2lab'));
    else
       A = colorspace('Lab<-RGB',A);
    end
    
    % Pre-compute Gaussian domain weights.
    [X,Y] = meshgrid(-w:w,-w:w);
    G = exp(-(X.^2+Y.^2)/(2*sigma_d^2));
    
    % Rescale range variance (using maximum luminance).
    sigma_r = 100*sigma_r;
    
    % Create waitbar.
    h = waitbar(0,'Applying bilateral filter...');
    set(h,'Name','Bilateral Filter Progress');
    
    % Apply bilateral filter.
    dim = size(A);
    B = zeros(dim);
    for i = 1:dim(1)
       for j = 1:dim(2)
          
             % Extract local region.
             iMin = max(i-w,1);
             iMax = min(i+w,dim(1));
             jMin = max(j-w,1);
             jMax = min(j+w,dim(2));
             I = A(iMin:iMax,jMin:jMax,:);
          
             % Compute Gaussian range weights.
             dL = I(:,:,1)-A(i,j,1);
             da = I(:,:,2)-A(i,j,2);
             db = I(:,:,3)-A(i,j,3);
             H = exp(-(dL.^2+da.^2+db.^2)/(2*sigma_r^2));
          
             % Calculate bilateral filter response.
             F = H.*G((iMin:iMax)-i+w+1,(jMin:jMax)-j+w+1);
             norm_F = sum(F(:));
             B(i,j,1) = sum(sum(F.*I(:,:,1)))/norm_F;
             B(i,j,2) = sum(sum(F.*I(:,:,2)))/norm_F;
             B(i,j,3) = sum(sum(F.*I(:,:,3)))/norm_F;
                    
       end
       waitbar(i/dim(1));
    end
    
    % Convert filtered image back to sRGB color space.
    if exist('applycform','file')
       B = applycform(B,makecform('lab2srgb'));
    else  
       B = colorspace('RGB<-Lab',B);
    end
    
    % Close waitbar.
    close(h);

cartoon.m

function C = cartoon(A)
    
    % CARTOON Image abstraction using bilateral filtering.
    %    This function uses the bilateral filter to abstract
    %    an image following the method outlined in:
    %
    %       Holger Winnemoller, Sven C. Olsen, and Bruce Gooch.
    %       Real-Time Video Abstraction. In Proceedings of ACM
    %       SIGGRAPH, 2006. 
    %
    %    C = cartoon(A) modifies the color image A to have a 
    %    cartoon-like appearance. A must be a double precision
    %    matrix of size NxMx3 with normalized values in the 
    %    closed interval [0,1]. Default filtering parameters
    %    are defined in CARTOON.M.
    %
    % Douglas R. Lanman, Brown University, September 2006.
    % dlanman@brown.edu, http://mesh.brown.edu/dlanman
    
    
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Set the image abstraction parameters.
    
    % Set bilateral filter parameters.
    w     = 5;         % bilateral filter half-width
    sigma = [3 0.1];   % bilateral filter standard deviations
    
    % Set image abstraction paramters.
    max_gradient      = 0.2;    % maximum gradient (for edges)
    sharpness_levels  = [3 14]; % soft quantization sharpness
    quant_levels      = 8;      % number of quantization levels
    min_edge_strength = 0.3;    % minimum gradient (for edges)
    
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    
    
    % Verify that the input image exists and is valid.
    if ~exist('A','var') || isempty(A)
       error('Input image A is undefined or invalid.');
    end
    if ~isfloat(A) || size(A,3) ~= 3 || ...
          min(A(:)) < 0 || max(A(:)) > 1
       error(['Input image A must be a double precision ',...
              'matrix of size NxMx3 on the closed ',...
              'interval [0,1].']);      
    end
    
    % Apply bilateral filter to input image.
    B = bfilter2(A,w,sigma);
    
    % Convert sRGB image to CIELab color space.
    if exist('applycform','file')
       B = applycform(B,makecform('srgb2lab'));
    else
       B = colorspace('Lab<-RGB',B);
    end
    
    % Determine gradient magnitude of luminance.
    [GX,GY] = gradient(B(:,:,1)/100);
    G = sqrt(GX.^2+GY.^2);
    G(G>max_gradient) = max_gradient;
    G = G/max_gradient;
    
    % Create a simple edge map using the gradient magnitudes.
    E = G; E(E<min_edge_strength) = 0;
    
    % Determine per-pixel "sharpening" parameter.
    S = diff(sharpness_levels)*G+sharpness_levels(1);
    
    % Apply soft luminance quantization.
    qB = B; dq = 100/(quant_levels-1);
    qB(:,:,1) = (1/dq)*qB(:,:,1);
    qB(:,:,1) = dq*round(qB(:,:,1));
    qB(:,:,1) = qB(:,:,1)+(dq/2)*tanh(S.*(B(:,:,1)-qB(:,:,1)));
    
    % Transform back to sRGB color space.
    if exist('applycform','file')
       Q = applycform(qB,makecform('lab2srgb'));
    else
       Q = colorspace('RGB<-Lab',qB);
    end
    
    % Add gradient edges to quantized bilaterally-filtered image.
    C = repmat(1-E,[1 1 3]).*Q;

colorspace.m

function varargout = colorspace(Conversion,varargin)
    %COLORSPACE  Convert a color image between color representations.
    %   B = COLORSPACE(S,A) converts the color representation of image A
    %   where S is a string specifying the conversion.  S tells the
    %   source and destination color spaces, S = 'dest<-src', or
    %   alternatively, S = 'src->dest'.  Supported color spaces are
    %
    %     'RGB'              R'G'B' Red Green Blue (ITU-R BT.709 gamma-corrected)
    %     'YPbPr'            Luma (ITU-R BT.601) + Chroma 
    %     'YCbCr'/'YCC'      Luma + Chroma ("digitized" version of Y'PbPr)
    %     'YUV'              NTSC PAL Y'UV Luma + Chroma
    %     'YIQ'              NTSC Y'IQ Luma + Chroma
    %     'YDbDr'            SECAM Y'DbDr Luma + Chroma
    %     'JPEGYCbCr'        JPEG-Y'CbCr Luma + Chroma
    %     'HSV'/'HSB'        Hue Saturation Value/Brightness
    %     'HSL'/'HLS'/'HSI'  Hue Saturation Luminance/Intensity
    %     'XYZ'              CIE XYZ
    %     'Lab'              CIE L*a*b* (CIELAB)
    %     'Luv'              CIE L*u*v* (CIELUV)
    %     'Lch'              CIE L*ch (CIELCH)
    %
    %  All conversions assume 2 degree observer and D65 illuminant.  Color
    %  space names are case insensitive.  When R'G'B' is the source or
    %  destination, it can be omitted. For example 'yuv<-' is short for
    %  'yuv<-rgb'.
    %
    %  MATLAB uses two standard data formats for R'G'B': double data with
    %  intensities in the range 0 to 1, and uint8 data with integer-valued
    %  intensities from 0 to 255.  As MATLAB's native datatype, double data is
    %  the natural choice, and the R'G'B' format used by colorspace.  However,
    %  for memory and computational performance, some functions also operate
    %  with uint8 R'G'B'.  Given uint8 R'G'B' color data, colorspace will
    %  first cast it to double R'G'B' before processing.
    %
    %  If A is an Mx3 array, like a colormap, B will also have size Mx3.
    %
    %  [B1,B2,B3] = COLORSPACE(S,A) specifies separate output channels.
    %  COLORSPACE(S,A1,A2,A3) specifies separate input channels.
    
    % Pascal Getreuer 2005-2006
    
    %%% Input parsing %%%
    if nargin < 2, error('Not enough input arguments.'); end
    [SrcSpace,DestSpace] = parse(Conversion);
    
    if nargin == 2
       Image = varargin{1};
    elseif nargin >= 3
       Image = cat(3,varargin{:});
    else
       error('Invalid number of input arguments.');
    end
    
    FlipDims = (size(Image,3) == 1);
    
    if FlipDims, Image = permute(Image,[1,3,2]); end
    if ~isa(Image,'double'), Image = double(Image)/255; end
    if size(Image,3) ~= 3, error('Invalid input size.'); end
    
    SrcT = gettransform(SrcSpace);
    DestT = gettransform(DestSpace);
    
    if ~ischar(SrcT) & ~ischar(DestT)
       % Both source and destination transforms are affine, so they
       % can be composed into one affine operation
       T = [DestT(:,1:3)*SrcT(:,1:3),DestT(:,1:3)*SrcT(:,4)+DestT(:,4)];      
       Temp = zeros(size(Image));
       Temp(:,:,1) = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
       Temp(:,:,2) = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
       Temp(:,:,3) = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
       Image = Temp;
    elseif ~ischar(DestT)
       Image = rgb(Image,SrcSpace);
       Temp = zeros(size(Image));
       Temp(:,:,1) = DestT(1)*Image(:,:,1) + DestT(4)*Image(:,:,2) + DestT(7)*Image(:,:,3) + DestT(10);
       Temp(:,:,2) = DestT(2)*Image(:,:,1) + DestT(5)*Image(:,:,2) + DestT(8)*Image(:,:,3) + DestT(11);
       Temp(:,:,3) = DestT(3)*Image(:,:,1) + DestT(6)*Image(:,:,2) + DestT(9)*Image(:,:,3) + DestT(12);
       Image = Temp;
    else
       Image = feval(DestT,Image,SrcSpace);
    end
    
    %%% Output format %%%
    if nargout > 1
       varargout = {Image(:,:,1),Image(:,:,2),Image(:,:,3)};
    else
       if FlipDims, Image = permute(Image,[1,3,2]); end
       varargout = {Image};
    end
    return;
    
    function [SrcSpace,DestSpace] = parse(Str)
    % Parse conversion argument
    
    if isstr(Str)
       Str = lower(strrep(strrep(Str,'-',''),' ',''));
       k = find(Str == '>');
       
       if length(k) == 1         % Interpret the form 'src->dest'
          SrcSpace = Str(1:k-1);
          DestSpace = Str(k+1:end);
       else
          k = find(Str == '<');
          
          if length(k) == 1      % Interpret the form 'dest<-src'
             DestSpace = Str(1:k-1);
             SrcSpace = Str(k+1:end);
          else
             error(['Invalid conversion, ''',Str,'''.']);
          end   
       end
       
       SrcSpace = alias(SrcSpace);
       DestSpace = alias(DestSpace);
    else
       SrcSpace = 1;             % No source pre-transform
       DestSpace = Conversion;
       if any(size(Conversion) ~= 3), error('Transformation matrix must be 3x3.'); end
    end
    return;
    
    function Space = alias(Space)
    Space = strrep(Space,'cie','');
    
    if isempty(Space)
       Space = 'rgb';
    end
    
    switch Space
    case {'ycbcr','ycc'}
       Space = 'ycbcr';
    case {'hsv','hsb'}
       Space = 'hsv';
    case {'hsl','hsi','hls'}
       Space = 'hsl';
    case {'rgb','yuv','yiq','ydbdr','ycbcr','jpegycbcr','xyz','lab','luv','lch'}
       return;
    end
    return;
    
    function T = gettransform(Space)
    % Get a colorspace transform: either a matrix describing an affine transform,
    % or a string referring to a conversion subroutine
    switch Space
    case 'ypbpr'
       T = [0.299,0.587,0.114,0;-0.1687367,-0.331264,0.5,0;0.5,-0.418688,-0.081312,0];
    case 'yuv'
       % R'G'B' to NTSC/PAL YUV
       % Wikipedia: http://en.wikipedia.org/wiki/YUV
       T = [0.299,0.587,0.114,0;-0.147,-0.289,0.436,0;0.615,-0.515,-0.100,0];
    case 'ydbdr'
       % R'G'B' to SECAM YDbDr
       % Wikipedia: http://en.wikipedia.org/wiki/YDbDr
       T = [0.299,0.587,0.114,0;-0.450,-0.883,1.333,0;-1.333,1.116,0.217,0];
    case 'yiq'
       % R'G'B' in [0,1] to NTSC YIQ in [0,1];[-0.595716,0.595716];[-0.522591,0.522591];
       % Wikipedia: http://en.wikipedia.org/wiki/YIQ
       T = [0.299,0.587,0.114,0;0.595716,-0.274453,-0.321263,0;0.211456,-0.522591,0.311135,0];
    case 'ycbcr'
       % R'G'B' (range [0,1]) to ITU-R BRT.601 (CCIR 601) Y'CbCr
       % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
       % Poynton, Equation 3, scaling of R'G'B to Y'PbPr conversion
       T = [65.481,128.553,24.966,16;-37.797,-74.203,112.0,128;112.0,-93.786,-18.214,128];
    case 'jpegycbcr'
       % Wikipedia: http://en.wikipedia.org/wiki/YCbCr
       T = [0.299,0.587,0.114,0;-0.168736,-0.331264,0.5,0.5;0.5,-0.418688,-0.081312,0.5]*255;
    case {'rgb','xyz','hsv','hsl','lab','luv','lch'}
       T = Space;
    otherwise
       error(['Unknown color space, ''',Space,'''.']);
    end
    return;
    
    function Image = rgb(Image,SrcSpace)
    % Convert to Rec. 709 R'G'B' from 'SrcSpace'
    switch SrcSpace
    case 'rgb'
       return;
    case 'hsv'
       % Convert HSV to R'G'B'
       Image = huetorgb((1 - Image(:,:,2)).*Image(:,:,3),Image(:,:,3),Image(:,:,1));
    case 'hsl'
       % Convert HSL to R'G'B'
       L = Image(:,:,3);
       Delta = Image(:,:,2).*min(L,1-L);
       Image = huetorgb(L-Delta,L+Delta,Image(:,:,1));
    case {'xyz','lab','luv','lch'}
       % Convert to CIE XYZ
       Image = xyz(Image,SrcSpace);
       % Convert XYZ to RGB
       T = [3.240479,-1.53715,-0.498535;-0.969256,1.875992,0.041556;0.055648,-0.204043,1.057311];
       R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3);  % R
       G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3);  % G
       B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3);  % B
       % Desaturate and rescale to constrain resulting RGB values to [0,1]   
       AddWhite = -min(min(min(R,G),B),0);
       Scale = max(max(max(R,G),B)+AddWhite,1);
       R = (R + AddWhite)./Scale;
       G = (G + AddWhite)./Scale;
       B = (B + AddWhite)./Scale;   
       % Apply gamma correction to convert RGB to Rec. 709 R'G'B'
       Image(:,:,1) = gammacorrection(R);  % R'
       Image(:,:,2) = gammacorrection(G);  % G'
       Image(:,:,3) = gammacorrection(B);  % B'
    otherwise  % Conversion is through an affine transform
       T = gettransform(SrcSpace);
       temp = inv(T(:,1:3));
       T = [temp,-temp*T(:,4)];
       R = T(1)*Image(:,:,1) + T(4)*Image(:,:,2) + T(7)*Image(:,:,3) + T(10);
       G = T(2)*Image(:,:,1) + T(5)*Image(:,:,2) + T(8)*Image(:,:,3) + T(11);
       B = T(3)*Image(:,:,1) + T(6)*Image(:,:,2) + T(9)*Image(:,:,3) + T(12);
       AddWhite = -min(min(min(R,G),B),0);
       Scale = max(max(max(R,G),B)+AddWhite,1);
       R = (R + AddWhite)./Scale;
       G = (G + AddWhite)./Scale;
       B = (B + AddWhite)./Scale;
       Image(:,:,1) = R;
       Image(:,:,2) = G;
       Image(:,:,3) = B;
    end
    
    % Clip to [0,1]
    Image = min(max(Image,0),1);
    return;
    
    function Image = xyz(Image,SrcSpace)
    % Convert to CIE XYZ from 'SrcSpace'
    WhitePoint = [0.950456,1,1.088754];  
    
    switch SrcSpace
    case 'xyz'
       return;
    case 'luv'
       % Convert CIE L*uv to XYZ
       WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
       WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
       L = Image(:,:,1);
       Y = (L + 16)/116;
       Y = invf(Y)*WhitePoint(2);
       U = Image(:,:,2)./(13*L + 1e-6*(L==0)) + WhitePointU;
       V = Image(:,:,3)./(13*L + 1e-6*(L==0)) + WhitePointV;
       Image(:,:,1) = -(9*Y.*U)./((U-4).*V - U.*V);                  % X
       Image(:,:,2) = Y;                                             % Y
       Image(:,:,3) = (9*Y - (15*V.*Y) - (V.*Image(:,:,1)))./(3*V);  % Z
    case {'lab','lch'}
       Image = lab(Image,SrcSpace);
       % Convert CIE L*ab to XYZ
       fY = (Image(:,:,1) + 16)/116;
       fX = fY + Image(:,:,2)/500;
       fZ = fY - Image(:,:,3)/200;
       Image(:,:,1) = WhitePoint(1)*invf(fX);  % X
       Image(:,:,2) = WhitePoint(2)*invf(fY);  % Y
       Image(:,:,3) = WhitePoint(3)*invf(fZ);  % Z
    otherwise   % Convert from some gamma-corrected space
       % Convert to Rec. 701 R'G'B'
       Image = rgb(Image,SrcSpace);
       % Undo gamma correction
       R = invgammacorrection(Image(:,:,1));
       G = invgammacorrection(Image(:,:,2));
       B = invgammacorrection(Image(:,:,3));
       % Convert RGB to XYZ
       T = inv([3.240479,-1.53715,-0.498535;-0.969256,1.875992,0.041556;0.055648,-0.204043,1.057311]);
       Image(:,:,1) = T(1)*R + T(4)*G + T(7)*B;  % X 
       Image(:,:,2) = T(2)*R + T(5)*G + T(8)*B;  % Y
       Image(:,:,3) = T(3)*R + T(6)*G + T(9)*B;  % Z
    end
    return;
    
    function Image = hsv(Image,SrcSpace)
    % Convert to HSV
    Image = rgb(Image,SrcSpace);
    V = max(Image,[],3);
    S = (V - min(Image,[],3))./(V + (V == 0));
    Image(:,:,1) = rgbtohue(Image);
    Image(:,:,2) = S;
    Image(:,:,3) = V;
    return;
    
    function Image = hsl(Image,SrcSpace)
    % Convert to HSL 
    switch SrcSpace
    case 'hsv'
       % Convert HSV to HSL   
       MaxVal = Image(:,:,3);
       MinVal = (1 - Image(:,:,2)).*MaxVal;
       L = 0.5*(MaxVal + MinVal);
       temp = min(L,1-L);
       Image(:,:,2) = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
       Image(:,:,3) = L;
    otherwise
       Image = rgb(Image,SrcSpace);  % Convert to Rec. 701 R'G'B'
       % Convert R'G'B' to HSL
       MinVal = min(Image,[],3);
       MaxVal = max(Image,[],3);
       L = 0.5*(MaxVal + MinVal);
       temp = min(L,1-L);
       S = 0.5*(MaxVal - MinVal)./(temp + (temp == 0));
       Image(:,:,1) = rgbtohue(Image);
       Image(:,:,2) = S;
       Image(:,:,3) = L;
    end
    return;
    
    function Image = lab(Image,SrcSpace)
    % Convert to CIE L*a*b* (CIELAB)
    WhitePoint = [0.950456,1,1.088754];
    
    switch SrcSpace
    case 'lab'
       return;
    case 'lch'
       % Convert CIE L*CH to CIE L*ab
       C = Image(:,:,2);
       Image(:,:,2) = cos(Image(:,:,3)*pi/180).*C;  % a*
       Image(:,:,3) = sin(Image(:,:,3)*pi/180).*C;  % b*
    otherwise
       Image = xyz(Image,SrcSpace);  % Convert to XYZ
       % Convert XYZ to CIE L*a*b*
       X = Image(:,:,1)/WhitePoint(1);
       Y = Image(:,:,2)/WhitePoint(2);
       Z = Image(:,:,3)/WhitePoint(3);
       fX = f(X);
       fY = f(Y);
       fZ = f(Z);
       Image(:,:,1) = 116*fY - 16;    % L*
       Image(:,:,2) = 500*(fX - fY);  % a*
       Image(:,:,3) = 200*(fY - fZ);  % b*
    end
    return;
    
    function Image = luv(Image,SrcSpace)
    % Convert to CIE L*u*v* (CIELUV)
    WhitePoint = [0.950456,1,1.088754];
    WhitePointU = (4*WhitePoint(1))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
    WhitePointV = (9*WhitePoint(2))./(WhitePoint(1) + 15*WhitePoint(2) + 3*WhitePoint(3));
    
    Image = xyz(Image,SrcSpace); % Convert to XYZ
    U = (4*Image(:,:,1))./(Image(:,:,1) + 15*Image(:,:,2) + 3*Image(:,:,3));
    V = (9*Image(:,:,2))./(Image(:,:,1) + 15*Image(:,:,2) + 3*Image(:,:,3));
    Y = Image(:,:,2)/WhitePoint(2);
    L = 116*f(Y) - 16;
    Image(:,:,1) = L;                        % L*
    Image(:,:,2) = 13*L.*(U - WhitePointU);  % u*
    Image(:,:,3) = 13*L.*(V - WhitePointV);  % v*
    return;  
    
    function Image = lch(Image,SrcSpace)
    % Convert to CIE L*ch
    Image = lab(Image,SrcSpace);  % Convert to CIE L*ab
    H = atan2(Image(:,:,3),Image(:,:,2));
    H = H*180/pi + 360*(H < 0);
    Image(:,:,2) = sqrt(Image(:,:,2).^2 + Image(:,:,3).^2);  % C
    Image(:,:,3) = H;                                        % H
    return;
    
    function Image = huetorgb(m0,m2,H)
    % Convert HSV or HSL hue to RGB
    N = size(H);
    H = min(max(H(:),0),360)/60;
    m0 = m0(:);
    m2 = m2(:);
    F = H - round(H/2)*2;
    M = [m0, m0 + (m2-m0).*abs(F), m2];
    Num = length(m0);
    j = [2 1 0;1 2 0;0 2 1;0 1 2;1 0 2;2 0 1;2 1 0]*Num;
    k = floor(H) + 1;
    Image = reshape([M(j(k,1)+(1:Num).'),M(j(k,2)+(1:Num).'),M(j(k,3)+(1:Num).')],[N,3]);
    return;
    
    function H = rgbtohue(Image)
    % Convert RGB to HSV or HSL hue
    [M,i] = sort(Image,3);
    i = i(:,:,3);
    Delta = M(:,:,3) - M(:,:,1);
    Delta = Delta + (Delta == 0);
    R = Image(:,:,1);
    G = Image(:,:,2);
    B = Image(:,:,3);
    H = zeros(size(R));
    k = (i == 1);
    H(k) = (G(k) - B(k))./Delta(k);
    k = (i == 2);
    H(k) = 2 + (B(k) - R(k))./Delta(k);
    k = (i == 3);
    H(k) = 4 + (R(k) - G(k))./Delta(k);
    H = 60*H + 360*(H < 0);
    H(Delta == 0) = nan;
    return;
    
    function Rp = gammacorrection(R)
    Rp = real(1.099*R.^0.45 - 0.099);
    i = (R < 0.018);
    Rp(i) = 4.5138*R(i);
    return;
    
    function R = invgammacorrection(Rp)
    R = real(((Rp + 0.099)/1.099).^(1/0.45));
    i = (R < 0.018);
    R(i) = Rp(i)/4.5138;
    return;
    
    function fY = f(Y)
    fY = real(Y.^(1/3));
    i = (Y < 0.008856);
    fY(i) = Y(i)*(841/108) + (4/29);
    return;
    
    function Y = invf(fY)
    Y = fY.^3;
    i = (Y < 0.008856);
    Y(i) = (fY(i) - 4/29)*(108/841);
    return;

runDemo.m

% RUNDEMO Illustrates the use of BFILTER2 and CARTOON.
    %    This demo shows typical usage for the bilateral 
    %    filter implemented by BFILTER2. The application
    %    of bilateral filtering to image abstraction is
    %    demonstrated by the CARTOON function.
    %
    % Douglas R. Lanman, Brown University, September 2006.
    % dlanman@brown.edu, http://mesh.brown.edu/dlanman
    
    % Load test images.
    % Note: Must be double precision in the interval [0,1].
    img1 = double(imread('einstein.jpg'))/255;
    img2 = double(imread('mandrill.jpg'))/255;
    img3 = double(imread('academy.jpg'))/255;
    
    % Introduce AWGN into test images.
    % Note: This will show the benefit of bilateral filtering.
    img1 = img1+0.03*randn(size(img1));
    img2 = img2+0.03*randn(size(img2));
    img1(img1<0) = 0; img1(img1>1) = 1;
    img2(img2<0) = 0; img2(img2>1) = 1;
    
    % Set bilateral filter parameters.
    w     = 5;       % bilateral filter half-width
    sigma = [3 0.1]; % bilateral filter standard deviations
    
    % Apply bilateral filter to each image.
    bflt_img1 = bfilter2(img1,w,sigma);
    bflt_img2 = bfilter2(img2,w,sigma);
    
    % Display grayscale input image and filtered output.
    figure(1); clf;
    set(gcf,'Name','Grayscale Bilateral Filtering Results');
    subplot(1,2,1); imagesc(img1);
    axis image; colormap gray;
    title('Input Image');
    subplot(1,2,2); imagesc(bflt_img1);
    axis image; colormap gray;
    title('Result of Bilateral Filtering');
    
    % Display color input image and filtered output.
    figure(2); clf;
    set(gcf,'Name','Color Bilateral Filtering Results');
    subplot(1,2,1); imagesc(img2);
    axis image; colormap gray;
    title('Input Image');
    subplot(1,2,2); imagesc(bflt_img2);
    axis image; title('Result of Bilateral Filtering');
    drawnow;
    
    % Apply bilateral filter for a "cartoon" effect.
    cartoon_img3 = cartoon(img3);
    
    % Display color input image and abstracted output.
    figure(3); clf;
    set(gcf,'Name','Image Abstraction Input');
    imagesc(img3); axis image;
    title('Input Image');
    figure(4); clf;
    set(gcf,'Name','Result of Image Abstraction');
    imagesc(cartoon_img3); axis image;
    title('Abstracted Image');

# 实例三：图像的模糊增强 #

实例图片：

![SouthEast 4][]

matlab源码：mhzq01.m

%
    % 图像的模糊增强
    %
    % 准备工作
    %
    x=imread('lena.bmp');
    [M,N]=size(x);
    x1=double(x);
    %
    % 基本参数
    %
    Fd=0.8;
    FD=-1*Fd;
    % Fe=128;
    Fe=128;
    Xmax=255;
    %
    % 模糊特征平面
    %
    for i=1:M
        for j=1:N
            P(i,j)=(1+(Xmax-x1(i,j))/Fe)^FD;
        end
    end
    %
    % 模糊增强
    %
    times=1;
    for k=1:times
        for i=1:M
            for j=1:N
                if P(i,j) <= 0.5000
                    P1(i,j)=2*P(i,j)^2;
                else
                    P1(i,j)=1-2*(1-P(i,j))^2;
                end
            end
        end
        P=P1;
    end
    %
    % 反模糊化
    %
    for i=1:M
        for j=1:N
            I(i,j)=Xmax-Fe*((1/P(i,j))^(1/Fd)-1);
        end
    end
    X=uint8(I);
    figure,imshow(x);
    figure,imshow(X);

[Center]: /images/20220731/ea1484714c9047c3a7974a4a300eb5df.png
[SouthEast]: /images/20220731/43350cb0d3f942f38d809779c9ee7a8c.png
[SouthEast 1]: /images/20220731/63cc780db31746be8901e3cf595566c9.png
[SouthEast 2]: /images/20220731/84b598d1e9b6495595bb40e106a0bc52.png
[Link 1]: http://www.baike.com/sowiki/%E9%AB%98%E6%96%AF%E6%BB%A4%E6%B3%A2?prd=content_doc_search
[Link 2]: http://www.baike.com/sowiki/%E5%87%BD%E6%95%B0?prd=content_doc_search
[SouthEast 3]: /images/20220731/7b9180e528d542c1af4f81454a72a7f3.png
[SouthEast 4]: /images/20220731/c794efd600174261ac663fb7514dc5a2.png