数字图像处理实验指导书(英文版)

EXPERIMENT 1 Showing and Orthogonal Transform of the Image一．Experimental purpose1.Master the orders of reading/writing and showing.2.Master the methods of transformations between the images of the differenttype.3.Master the methods of the orthogonal transform and the inverse transform.二．Be familiar with the common orders as follows：(skillful mastery)1.The orders of reading/writing and showing:imread read the image fileimfinfo read the related information of the image fileimwrite output the imageImshow show function of the standard imageImage establish and show a image objectImagesc adjust automatically the value field and show theimageColorbar show the color bartheimageMontage spliceImmovie turn the image sequence composed by the index colorimage into the cartoonSubimage show the multi-piece images in a graph windowzoom the image zoomwarp show the image in a curved face by the texturemapping2. The transform orders of image type:Dither image dither, turn the grey scale image into the binaryimage or dither the real color image into the index imageGray2ind turn the grey scale image into the index imageGrayslice turn the grey scale image into the index image by settingthe field valueIm2bw turn the real color, the index color and the grey scale imageinto the binary image by setting the luminance field valueInd2gray turn the index color image into the grey scale imageInd2rgb turn the index color image into the real color imageMat2gray turn the data matrix into the grey scale imageRgb2gray turn the real color image into the grey scale imageRgb2ind turn the real color image into the index color image3. The orders of the orthogonal transform and the inverse transformfft2 two-dimension fft transform ifft2 two-dimension fftinverse transformfftn N-dimensionfft transformifftn N-dimension fft inverse transform fftshift move the center of the spectrum by fft,fft2 and fftn transformsinto the center of the matrix or the vectordct2 two-dimension dct transform idct2 two-dimension dctinverse transform三．Experimental contents:1. read/write and show the image:① Read cameraman.tif file;② Examine the related information of the image and point out the file layout and the type ofthe image;③ Show the image and observe its zoom;④ Show the color bar;⑤ Show the image in imagesc function. Its grey-scale range is 64-128;⑥ Map cameraman.tif into the surface of the cylinder in the warp order;⑦ Load the mri images in the load order and show these images in the montage function,then, turn these image into the cartoon and show the cartoon in the movie function. Attention: the immovie function is applied only to the index image.Cue: load mrimontage (data matrix of the mri, the color map matrix of the mri)mov ＝immovie (data matrix of the mri, the color map matrix of the mri)colormap （map ）movie （mov ）2. Transformations between the images of the different type① Turn the RGB image flowers.tif into the grey scale image. Show and compare the twoimages in the same image window;② Turn the index image chess.mat into the grey scale image. Show and compare the twoimages in the same image window;③ Turn the grey scale image cameraman.tif into the binary image (realize individually inthe im2bw and the dither), show and compare the three images in the same image window;④ Turn the grey scale image pout.tif into the index image X (the corresponding colorimages are the gray (128) and gray (16) ). Show and compare the three images in the same image window;3. The orthogonal transform and inverse transform①Calculate two-dimension fast Fourier transform of the saturn2 image and show its spectrum amplitude .Cue: extract the image: load imdemos saturn2②Do the DCT transform to the saturn2 image.③Do two-dimension hadamard transform to the saturn2 image.Cue: firstly: create a two-dimension hadamard orthogonal matrix in the matlab, then,image data multiply the orthogonal matrix.④What are differences of the ①②③ transformations in the energy focus?⑤Remake ①—④ to the autumn.tif image.Cue: need to turn the image into the grey scale image.四．Thought after the class1. Which are the file layouts of image? How do these transformations each other? Which are image types? How do these transformations each other? How do the images of the different type show? Which are the data matrixes of image? How do these transformations each other?2. Which types of image can not be used directly to image processing?EXPERIMENT 2 Image Enhancement 一．Experimental purpose1. Master the fundamental method of the image enhancement and observe the results of theimage enhancement;2. Be familiar with the denoise method of the image processing;3. Be familiar with the transformations of the file layouts and of the color systems;二．Be familiar with the common orders as follows:imadust adjust the contrast by the histogram transformationhisteq histogram equalizationhist show the histogram of the image dataimnoise add the representative noise to the image（gaussian,salt&pepper, speckle）medfilt2 two-dimension median filteringordfilt2 two-dimension sequential statistic filteringwiener2 two-dimension Wiener filtering三．Experimental contents:1.Contrast enhancement① Read the cameraman.tif file;② Show the original image;③ Show the histogram of the image;④ Enhance the contrast of image by the histeq⑤ Show the histogram of the image after the contrast and compare it with the histogram ofthe list ③;⑥ Show the image after the contrast and compare it with the original image.四． Image smoothing① Read the eight,tif file. Show the images added individually by the Gaussiannoise, Salt & pepper noise and multiplicative noise and compare these images.② Three previous images added by the noise make individually two-dimensionmedian filtering, two-dimension sequential statistic filtering and two-dimension Wiener filtering③Compare the original image with the image polluted by the noise and the image after thefilter. Think that which filter is effective to suppress the corresponding noise and the influence of the neighborhood to suppressing noise and image blurring.五． The color of the RGB image flowers.tif becomes more clear and bright.①Read the flower.tif file;②Show the image;③Color space transformation. Turn the RGB color space into the HSV color space(transform function is rgb2hsv )④Enhance saturation component S by the histogram equalization in the HSV color space;⑤Return the HSV color space into the RGB color space;⑥Compare the images transformed previously with the original image and observe whetherthese become more clear and bright.六． Thought after the classWhy the image is dealt with by the Histogram Equalization? Which methods of the image enhancement you can think?EXPERIMENT 3 Boundary Detection and Texture Detection一. Experimental purpose1. Be familiar with the common orders of the boundary detection and boundary detection operator;2. Be familiar with the methods of the boundary detection.二. Be familiar with the common orders as follows:Edgethe boundary detection Sobel Sobel operatorCanny a antinoise and keeping weak boundary operator Robert Robert operatorPrewitt Prewitt operatorLog Log operator(Marr)blkproc block treatingstd22D standard deviation imhist image grey scale statistic三. Experimental contents:1. Boundary detection① Read the rice.tif file;② Show the image;③ Show five images detected individually in the Sober operator, Robert operator, Prewitt operator, Log operator, Canny operator and observe the difference of connectivity in the difference operators. Find the effective method of boundary detection to the rice.tif file. Cue: BW=edge (data matrix of the image, ‘operator’)④ Reduplicate the operation of ①②③ operations to the Saturn.tif image.⑤Help menu->demos->toolboxes->image processing->morphology, analysis and segmentation->egde detection in the matlab. Operate the demo program. Observe the value of the boundary detection by choosing the different images and operators.2. Texture detectionTexture detection can be realized by the method of local grey-scale statistic. The common methods have 2D standard deviation measurement and information entropy measurement. Texture detection is the method of the local detection. So, the texture detection can be realized by the blocking method. Computing formulas of standard deviation and information entropy are()p E is the grey scale mean. k p is the probability of the k grade grey scale.Compute the information entropy of the image by programming function and analyze the texture of the 4×4 block,8×8 block to image in the texture analyse of information entropy.()[]∑∞=−=12k k k p p E x D ∑−=kkk p p E logCue: Refer to the following example during the programming. Attention: firstly, Program information entropy function yourself. (MATLAB supplies 2D standard deviation function in the command line fun=@std2 )Example: texture analyse of the 8×8 sub-block to image in 2D standard deviation measurement.man=imread('cameraman.tif');fun=@std2; % std2 is the 2D standard deviation function.text1=blkproc(man,[8 8],fun);text=imresize(text1,[256 256],'bicubic');subplot(121);imshow(man);title('cameraman');subplot(122);imshow(text,[]); title('8X8 std');EXPERIMENT 4 Image Compression Encoding一. Experimental purpose1. Be familiar with the fundamental of the image compression encoding;2. Be familiar with the fundamental performance of the orthogonal image compression encoding.二.Be familiar with the common orders as follows:dct2 - Compute 2-D discrete cosine transformdctmtx - Compute discrete cosine transform matrix.fft2 - Compute 2-D fast Fourier transform .fftshift - Reverse quadrants of output of FFT.idct2 - Compute 2-D inverse discrete cosine transform.ifft2 - Compute 2-D inverse fast Fourier transform.Hadamard - Hadamard matrix.bestblk - Choose block size for block processing.blkproc - Implement distinct block processing for image.col2im - Rearrange matrix columns into blocks.im2col - Rearrange image blocks into columns.三.Experimental contents:1. Compress the image in the FFT transformation;①Read the rice.tif file;②Normalize the image;③Show the original image;④The image compression ratio is 4:1;⑤Separate the image to the 16×16 sub-image and make the FFT transform;⑥Realign the transform coefficient matrix and Sequence the coefficient matrix;⑦Reserve the higher-order coefficient according to the compression ratio;⑧Realign the coefficient matrix⑨Obtain the recovery images of the sub-images by the FFT inverse transform to sub-images;⑩Show the image compressed and compare it with the original image.2. Compress individually the image in the DCT and HT transforms according to the upper steps. Cue: the orders of the blocking and Hadamard transforms in the Hadamard transform. areT＝ hadamarda(image blocking size)；for example, the image is separated 16×16 blocking, so the blocking size is 16.Hdcoe=blkproc(the image normalized，[16 16]),’P1*x*P2’,T,T)。