数字图像(实验四).doc

福建农林大学计算机与信息学院实验报告课程名称：数字图像处理（双语）（Digital Image Processing）姓 名： 系：计算机科学与技术专 业：计算机科学与技术年 级： 指导教师：王长缨职 称：副教授20142015学年下学期福建农林大学计算机与信息学院实验报告系： 计算机科学与技术 专业：计算机科学与技术 年级：2012级 姓名： 学号： 实验室号 田家炳510 计算机号 实验时间：2015年5月 日 指导教师签字： 成绩： Experiment IV Noise reduction with adaptive filtering (validated)（2 periods）Objective Establish the data driven idea from adaptive filter in both spatial and frequency domain. MethodologyApply spatial adaptive filter and Wiener filter to a noised photo respectively and make a comment by comparison of two methods. Environment Hardware: General PC Operating system: Windows XP Calculating platform: MATLAB 6.5 or higher versionExperiment in detail:1Design an adaptive local noise reduction filter and apply it to an image with Gaussian noise. Compare the performance of the adaptive local noise reduction filter with arithmetic mean and geometric mean filter. 代码:rt=imread(D:tupian1.jpg); gray=rgb2gray(rt);subplot(2,3,1);imshow(rt);title(Original picture) ; subplot(2,3,2);imshow(gray);title(Gray picture) ;rtg=im2double(gray);rtg=imnoise(rtg,gaussian,0,0.005)%加入均值为0，方差为0.005的高斯噪声subplot(2,3,3);imshow(rtg);title(Gaussian picture);a,b=size(rtg);n=3;smax=7;nrt=zeros(a+(smax-1),b+(smax-1);for i=(smax-1)/2+1):(a+(smax-1)/2) for j=(smax-1)/2+1):(b+(smax-1)/2) nrt(i,j)=rtg(i-(smax-1)/2,j-(smax-1)/2); endendfigure;imshow(nrt);title(扩充后的图像);nrt2=zeros(a,b); for i=n+1:a+n for j=n+1:b+n for m1=3:2 m2=(m1-1)/2; c=nrt2(i-m2:i+m2,j-m2:j+m2);%使用7*7的滤波器 Zmed=median(median(c); Zmin=min(min(c); Zmax=max(max(c); A1=Zmed-Zmin; A2=Zmed-Zmax; if(A10&A20&B20) nrt2(i,j)= nrt2(i,j); else nrt2(i,j)=Zmed; end continue; end end endend nrt3=im2uint8(nrt2);figure;imshow(nrt3);title(adaptive median filter);2. Implement Wiener filter with “wiener2” function of MatLab to an image with Gaussian noise and compare the performance with adaptive local noise reduction filter.3.Experimental resultData in whatever chart, image and other form is required with explanations in English respectively. Ques