數(shù)字圖像處理實(shí)驗(yàn)報(bào)告

1、實(shí)驗(yàn)一 圖像增強(qiáng)與平滑一實(shí)驗(yàn)?zāi)康募耙?了解matlab的操作環(huán)境和基本功能。2掌握matlab中圖像增強(qiáng)與平滑的函數(shù)的使用方法。3加深理解圖像增強(qiáng)與平滑的算法原理。二、實(shí)驗(yàn)內(nèi)容（一）研究以下程序，分析程序功能；輸入執(zhí)行各命令行，認(rèn)真觀察命令執(zhí)行的結(jié)果。熟悉程序中所使用函數(shù)的調(diào)用方法，改變有關(guān)參數(shù)，觀察試驗(yàn)結(jié)果。（可將每段程序保存為一個(gè).m文件）1直方圖均衡clear all; close all % clear the matlab workspace of any variables % and close open figure windows。i = imread(pout.tif);

2、 % reads the sample images pout.tif, and stores it in imshow(i) % an array named i.display the imagefigure, imhist(i) % create a histogram of the image and display it in % a new figure window.i2,t = histeq(i); % histogram equalization.figure, imshow(i2) % display the new equalized image, i2, in a ne

3、w figure window.figure, imhist(i2) % create a histogram of the equalized image i2.figure,plot(0:255)/255,t); % plot the transformation curve.imwrite (i2, pout2.png); % write the newly adjusted image i2 to a disk file named% pout2.png.imfinfo(pout2.png) % check the contents of the newly written file執(zhí)

4、行結(jié)果如下：command窗口：ans = filename: pout2.png filemoddate: 29-apr-2006 15:33:34 filesize: 36938 format: png formatversion: width: 240 height: 291 bitdepth: 8 colortype: grayscale formatsignature: 137 80 78 71 13 10 26 10 colormap: histogram: interlacetype: none transparency: none simpletransparencydata:

5、 backgroundcolor: renderingintent: chromaticities: gamma: xresolution: yresolution: resolutionunit: xoffset: yoffset: offsetunit: significantbits: imagemodtime: 29 apr 2006 07:33:34 +0000 title: author: description: copyright: creationtime: software: disclaimer: warning: source: comment: othertext:

6、上述命令窗口顯示的是圖片pout2.png的相關(guān)信息，包括文件名- pout2.png、文件最后修改時(shí)間-29-apr-2006 15:33:34、文件大小-36938、圖片格式-png、格式版本、圖片寬度-240、圖片高度-291、圖像像素值所占用的二進(jìn)制位數(shù)-8、圖像類型- grayscale等等。下面是所顯示的圖像:原圖像 原圖像的灰度直方圖利用直方圖均衡化函數(shù)處理后的圖像 圖像被均衡化后的灰度直方圖 matlab均衡化函數(shù)的函數(shù)曲線圖 以.png格式保存后的圖像對(duì)實(shí)驗(yàn)結(jié)果分析如下：由以上有關(guān)圖像對(duì)比可以看出，經(jīng)過均衡化的圖像比原圖像更富有層次感，對(duì)比度更加明顯，圖像效果也顯得更為好一些

7、。從原圖像的灰度直方圖和變換后的灰度直方圖可以看出，均衡化函數(shù)拉伸了原圖像灰度值較為密集的部分（大約在75-150歸一化為0.3-0.6），從而使圖像的灰度范圍得以擴(kuò)大，灰度值更加均勻而非原圖像的集中分布，所以原圖像卻是灰蒙蒙的，變換后的圖像顯得非常清亮。注意：imadjust()功能：調(diào)整圖像灰度值或顏色映像表，也可實(shí)現(xiàn)伽馬校正。語法：j = imadjust(i,low_in high_in,low_out high_out,gamma)newmap = imadjust(map,low_in high_in,low_out high_out,gamma)rgb2 = imadjust(r

8、gb1,.)2直接灰度變換clear all; close alli = imread(cameraman.tif);j = imadjust(i,0 0.2,0.5 1);imshow(i)figure, imshow(j)x,map = imread(forest.tif);figure,imshow(x,map)i2 = ind2gray(x,map);j2 = imadjust(i2,0.5); figure,imshow(i2)figure, imshow(j2)j3 = imadjust(i2,1.5); figure, imshow(j3)help imadjust % disp

9、lay the imadjust() function information.程序執(zhí)行結(jié)果如下：command窗口：imadjust adjust image intensity values or colormap. j = imadjust(i,low_in high_in,low_out high_out,gamma) maps the values in intensity image i to new values in j such that values between low_in and high_in map to values between low_out and h

10、igh_out. values below low_in and above high_in are clipped; that is, values below low_in map to low_out, and those above high_in map to high_out. you can use an empty matrix () for low_in high_in or for low_out high_out to specify the default of 0 1. gamma specifies the shape of the curve describing

11、 the relationship between the values in i and j. if gamma is less than 1, the mapping is weighted toward higher (brighter) output values. if gamma is greater than 1, the mapping is weighted toward lower (darker) output values. if you omit the argument, gamma defaults to 1 (linear mapping). newmap =

12、imadjust(map,low_in; high_in,low_out; high_out,gamma) transforms the colormap associated with an indexed image. if low_in, high_in, low_out, high_out, and gamma are scalars, then the same mapping applies to red, green and blue components. unique mappings for each color component are possible when: l

13、ow_in and high_in are both 1-by-3 vectors, low_out and high_out are both 1-by-3 vectors, or gamma is a 1-by-3 vector. the rescaled colormap, newmap, is the same size as map. rgb2 = imadjust(rgb1,.) performs the adjustment on each image plane (red, green, and blue) of the rgb image rgb1. as with the

14、colormap adjustment, you can apply unique mappings to each plane. note that if high_out low_out, the output image is reversed, as in a photographic negative. the function stretchlim can be used with imadjust to apply an automatically computed contrast stretch. class support - for syntaxes that inclu

15、de an input image (rather than a colormap), the input image can be of class uint8, uint16, or double. the output image has the same class as the input image. for syntaxes that include a colormap, the input and output colormaps are of class double. examples - i = imread(pout.tif); j = imadjust(i,0.3

16、0.7,); imshow(i), figure, imshow(j) rgb1 = imread(flowers.tif); rgb2 = imadjust(rgb1,.2 .3 0; .6 .7 1,); imshow(rgb1), figure, imshow(rgb2) see also brighten, histeq, stretchlim.下面是所顯示的圖像: 原圖像 灰度調(diào)整后的圖像原彩色圖像 原灰度圖像 參數(shù)為0.5的灰度調(diào)整函數(shù)調(diào)整后的結(jié)果 參數(shù)為1.5的灰度調(diào)整函數(shù)調(diào)整后的結(jié)果對(duì)實(shí)驗(yàn)結(jié)果分析如下：通過查看命令窗口所顯示的信息，對(duì)imadjus有所了解，以來分析函數(shù)作用的結(jié)

17、果。對(duì)于圖像cameraman.tif， imadjust共有四個(gè)參數(shù)，程序中對(duì)該圖像調(diào)整時(shí)，默認(rèn)第四個(gè)參數(shù)為1，即進(jìn)行線性變換。函數(shù)將0 0.2范圍內(nèi)的灰度對(duì)應(yīng)到0.5 1，其余像素值不變，即0對(duì)應(yīng)0.5,0.2對(duì)應(yīng)1，其余像素值可根據(jù)直線方程類推，通過調(diào)整，圖像中大于0.5的像素值大量增多，而小于0.2的像素經(jīng)過調(diào)整已經(jīng)沒有了，所以整個(gè)圖像顯得比原圖像明亮。對(duì)圖像forest.tif，imadjust的第四個(gè)參數(shù)分別設(shè)置為0.5和1.5.上面已經(jīng)提到如果第四個(gè)參數(shù)設(shè)置為1，即進(jìn)行線性變換，而如果小于1則增強(qiáng)低灰度范圍，相應(yīng)的大于1則增強(qiáng)高灰度范圍。由上面的第5、6、7幅圖可以看出，參數(shù)設(shè)置

18、為0.5的圖像明顯比原灰度圖像偏亮，而參數(shù)設(shè)置為1.5的圖像與原灰度圖像的效果差不多。這是因?yàn)?，參?shù)設(shè)置為0.5時(shí)，增強(qiáng)了低灰度值，從而使低灰度值的像素減少，高灰度值的像素增多，從而使圖像變亮。而參數(shù)設(shè)置為1.5時(shí)，增強(qiáng)的是高灰度范圍，對(duì)于低灰度范圍沒有影響，而且本身高灰度值效果就偏亮，所以函數(shù)作用效果有限，所以看上去與原灰度圖像效果差不多。3空域平滑濾波（模糊、去噪）clear all; close alli = imread(eight.tif);h1 = ones(3,3) / 9;h2 = ones(5,5) / 25;i1 = imfilter(i,h1);i2 = imfilter

19、(i,h2);figure(1), imshow(i), title(original image);figure(2), imshow(i1), title(filtered image with 3*3 )figure(3), imshow(i2), title(filtered image with 5*5 )j1 = imnoise(i,gaussian,0,0.005); % 加入gaussian 噪聲j2 = imnoise(i,salt & pepper,0.02); % 加入椒鹽噪聲% 對(duì)j1、j2進(jìn)行平均值平滑濾波k1 = imfilter(j1,fspecial(avera

20、ge,3);k2 = imfilter(j2,fspecial(average,3);figure(4);subplot(2,2,1), imshow(j1) , title(gaussian);subplot(2,2,2), imshow(j2), title(salt & pepper );subplot(2,2,3), imshow(k1), title(average );subplot(2,2,4), imshow(k2);% 對(duì)j1、j2進(jìn)行中值濾波k3 = medfilt2(j1,3 3);k4 = medfilt2(j2,3 3);figure(5);subplot(2,2,1

21、), imshow(j1) , title(gaussian);subplot(2,2,2), imshow(j2), title(salt & pepper );subplot(2,2,3), imshow(k3), title( median filtering );subplot(2,2,4), imshow(k4)程序執(zhí)行結(jié)果如下：對(duì)以上實(shí)驗(yàn)結(jié)果分析如下： 由于沒有添加噪聲，所以看不出濾波效果。但是從上圖可以看出，濾波后圖像有點(diǎn)模糊，而使用5*5的鄰域窗口濾波后的圖像模糊的更加明顯。這是因?yàn)?，均值濾波采用計(jì)算鄰域窗口的均值代替自身的像素值來濾波，鄰域窗口越大，參與平均計(jì)算的像素增多，雖

22、然濾波效果也更為明顯，特別是針對(duì)顆粒狀噪聲有很好的濾除效果，但同樣也會(huì)使圖像中物體的像素值產(chǎn)生更大的變化，反映在圖像上就是物體顯得更加模糊。 對(duì)以上實(shí)驗(yàn)結(jié)果分析如下： 中值濾波采用的是鄰域的中值代替像素原來的值，所以原像素值受異常值的影響相對(duì)均值濾波要小的多，由中值濾波后的圖像可以看出，圖像并非像均值濾波那樣顯得模糊。下面比較一下均值濾波和中值濾波。對(duì)于同為3*3鄰域窗口，對(duì)于椒鹽噪聲，中值濾波效果要好于均值濾波，而對(duì)于高斯噪聲兩者差不多。對(duì)于椒鹽噪聲，由均值濾波效果可以看出，噪聲明顯接近背景顏色，有一定濾波效果，如果鄰域窗口變成5*5的一定會(huì)對(duì)噪聲起到更好的濾除作用,但負(fù)面作用是會(huì)使圖像變得

23、更加模糊。而中值濾波，由于采用的中值，所以對(duì)于顆粒狀的物體也可以很好的濾出。而對(duì)于高斯噪聲，噪聲符合一定分布，均值、中值對(duì)像素值的作用有限，所以雖然有一定的濾波作用，但并不能徹底消除噪聲，可以嘗試維納濾波等其它濾波方法。4空域銳化濾波clear all; close all;i = imread(moon.tif);w=fspecial(laplacian,0),w8=1,1,1;1,-8,1;1,1,1i1= imfilter(i,w, replicate);figure(1); imshow(i), title(original image);figure(2), imshow(i1),

24、title(laplacian image);f = im2double(i);f1= imfilter(f,w, replicate);figure(3), imshow(f1,), title(laplacian image);f2= imfilter(f,w8, replicate);f4 = f-f1;f8 = f-f2;figure(4), imshow(f4);figure(5), imshow(f8); f4=f-f1 f8=f-f2對(duì)以上實(shí)驗(yàn)結(jié)果分析如下：程序中先對(duì)原圖像采用拉普拉斯算子對(duì)原圖像進(jìn)行銳化，然后再將圖像像素值轉(zhuǎn)化成double類型，之后再進(jìn)行拉普拉斯算子和一個(gè)自編的算子進(jìn)行銳化。由上圖可以看出，兩個(gè)拉普拉斯濾波后的圖像雖然亮度不一樣，但是特征并沒有多少差異，這是因?yàn)橄袼刂缔D(zhuǎn)化為double類型時(shí)，像素值進(jìn)行了相應(yīng)變化。由于算子本身有濾波作用，而且可以增強(qiáng)圖像的邊緣輪廓，所以計(jì)算f4 = f-f1;f8 = f-f2;然后再顯示f4和f8，具體的邊緣輪廓細(xì)節(jié)較原圖像效果要好很多。5圖像的偽彩色處理密度分割clear all, close alli = imread(ngc4024m.tif);x = grayslice(i,