1.軟件版本

MATLAB2021a
2.本算法理論知識(shí)

??????? 提出了一種”基于小波變換的多尺度自適應(yīng)雙邊濾波器“算法。

??????? 其對應(yīng)的算法流程如下所示：

?????? 下面，我們從理論上限介紹一下這里所采用的改進(jìn)后的算法。

第一：多尺度圖像的自適應(yīng)雙邊濾波

??????? 這個(gè)部分，是我們這里所需要研究的創(chuàng)新的算法，這里重點(diǎn)從理論上介紹一下。

首先通過圖像亮度，將圖像區(qū)分為前景圖和背景圖。

?????? 這里，我們主要通過二值話處理，進(jìn)行圖像的前景和背景的區(qū)分，這個(gè)部分的理論為：

?這里，門限T的計(jì)算，我們主要通過matlab自帶的一個(gè)函數(shù)獲得，這個(gè)函數(shù)會(huì)根據(jù)每個(gè)圖像自動(dòng)計(jì)算出門限T。

graythresh

對于的代碼為：

?然后分別對前景和背景進(jìn)行sigma參數(shù)的自適應(yīng)調(diào)整。

?????? 這里，sigma的計(jì)算公式為：

???????

?這里，我們根據(jù)上面的背景前景，做如下的設(shè)置。

? ? ?這里，我們分別對前景和背景下乘以系數(shù)K1和K2，其中根據(jù)亮度分布（亮的為背景、暗的為物體）來確定BF的各像素的兩個(gè)sigma值，讓亮區(qū)域平滑更多（選用大sigma），暗區(qū)域平滑偏小（小sigma）。

3.核心代碼

% 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(:)) > 1error(['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 < 1w = 5; end w = ceil(w);% Verify bilateral filter standard deviations. if ~exist('sigma','var') || isempty(sigma) || ...numel(sigma) ~= 2 || sigma(1) <= 0 || sigma(2) <= 0sigma = [3 0.1]; end% Apply either grayscale or color bilateral filtering. if size(A,3) == 1B = bfltGray(A,w,sigma(1),sigma(2)); elseB = 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(:));endwaitbar(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')); elseA = 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;endwaitbar(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);

4.操作步驟與仿真結(jié)論

?

5.參考文獻(xiàn)

??A25-09
6.完整源碼獲得方式

方式1：微信或者QQ聯(lián)系博主

方式2：訂閱MATLAB/FPGA教程，免費(fèi)獲得教程案例以及任意2份完整源碼

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