您的位置：首页 > 其它

冈萨雷斯数字图像处理——彩色图像增强3实例

2015-12-26 14:01 495 查看

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

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：

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，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：

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：

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);

内容来自用户分享和网络整理，不保证内容的准确性，如有侵权内容，可联系管理员处理

标签：

相关文章推荐

新的分享

章节导航