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1、Digital Image ProcessingComputer Project Report IVContrast Enhancement, Histogram Equalization, Spatial Filtering, & Edge DetectionStudent ID: 20091613310032Name: Xiaopeng JiDate: April 13, 2012A. Objectivesi. Study the effects of contrast enhancement histogram equalization and histogram stretching.
2、ii. Study the effects of histogram equalization via HISTEQ function.iii. Utilizing the SUBPLOT function to display images.iv. Study the effects of spatial filtering using IMNOISE, FSPECIAL, and MEDFILT2 functions.v. Study image noise and how the median and average filters remove noise.vi. Study diff
3、erent methods of edge detection for use on noisy images specifically motion blur, Gaussian noise and filtered Gaussian noise via averaging.vii. Formulate real-world applications which utilize the above functions and concepts.B. MethodsThis project will utilize the functions of MATLAB Image Processin
4、g Toolbox to perform Contrast Enhancement, Histogram Equalization, Spatial Filtering using averaging and median filters, and an Edge detection algorithm experiment.1. IMRESIZE function utilization for image expansion.2. Contrast stretching using IMADJUST and STRETCHLIM functions.3. Histogram equaliz
5、ation using HISTEQ function.4. Data display utilizing SUBPLOT function.5. Spatial filtering using IMNOISE, FSPECIAL, and MEDFILT2.6. Edge detection on noisy images using Matlab noise and edge detection functions.C. Results1. Load image saltAndPepper.gif and use the IMRESIZE function to interpolate b
6、y a factor of 4 using bi-cubic interpolation.At the command prompt enter : saltandpepper_filename=saltAndPepper.gif;saltandpepper_image=imread(saltandpepper_filename);picture_saltandpepper_scale=imresize(saltandpepper_image,60*4 59*4,bicubic);imwrite(picture_saltandpepper_scale,saltandpepper_bicubic
7、.jpg, jpg);The original image, saltAndPepper.gif, was spatially expanded 4x in each dimension using bi-cubic interpolation. The IMRESIZE function was utilized to perform the operation.Figure 1: Original image.Figure2: saltAndPepper.gif spatially expanded by 4x.2. Contrast Stretching Use the MATLAB f
8、unction IMADJUST to perform the contrast stretch.a) Use imadjust(Image, LOW_IN, HIGH_IN,); in this format. LOW_IN, HIGH_IN must be in the 0,1 range so the vector must be scaled by the maximum gray level that the image is quantized to (i.e. 8-bits a 256 gray levels).At the command prompt enter :salta
9、ndpepper_imadjust=imadjust(picture_saltandpepper_cale,0 (180/255),);imwrite(saltandpepper_imadjust,saltandpepper_imadjust.jpg,jpg);Figure 3: Adjusted figure 2 such that the lowest and highest intensities are 0 and 255 respectively.b) Compute the mean gray level (function: MEAN2) and the standard dev
10、iation (function: STD2) and design a mapping function for LOW_IN and HIGH_IN based on the following equations: LOW_IN=(mean-(standard deviation)/2)/256 HIGH_IN=(mean+(standard deviation)/2)/256At the command prompt enter : saltandpepper_mean=mean2(picture_saltandpepper_scale)saltandpepper_std=std2(p
11、icture_saltandpepper_scale)low_in=(saltandpepper_mean-(saltandpepper_std)/2)/256high_in=(saltandpepper_mean+(saltandpepper_std)/2)/256saltandpepper_mean=imadjust(picture_saltandpepper_scale,low_in high_in,);imwrite(saltandpepper_mean,saltandpepper_mean.jpg,jpg);Figure 2: The histogram of cameraman.t
12、ifsaltandpepper_mean = 60.8439saltandpepper_std = 30.3702low_in = 0.1784high_in = 0.2970Figure 4: IMADJUST performed on Figure 2 with LOW_IN and HIGH_IN equal to above calculationsc) Use the function STRETCHLIM to compute the tolerance for the IMADJUST function. The function STRETCHLIM will compute
13、a vector corresponding to LOW_IN, HIGH_IN. Use the vector calculated by STRETCHLIM to in the IMADJUST function described in a.) and compute the image transformation.At the command prompt enter : low_high = stretchlim(picture_saltandpepper_scale, 0)saltandpepper_stretchlim=imadjust(picture_saltandpep
14、per_scale,low_high, );imwrite(saltandpepper_stretchlim , saltandpepper_stretchlim.jpg, jpg);low_high = (0 0.7059)Figure 5: STRETCHLIM performed with IMADJUST on Figure 23. Histogram Equalization Use the function HISTEQ to compute the histogram equalization of the original image.4. Data Display Use t
15、he SUBPLOT command to display your results.At the command prompt enter: imhist(picture_saltandpepper_scale);saltandpepper_EqualizedImage = histeq(picture_saltandpepper_scale);imwrite(saltandpepper_EqualizedImage,saltandpepper_EqualizedImage.jpg, jpg); colormap(gray);subplot(2,2,1);image(picture_saltandpepper_scale);title(Original Image);subplot(2,2,2);image(saltandpepper_mean);title(Mean and Std Image);subplot(2,2,3);image(saltandpepper_stretchlim);title(StretchLim Image);subplot(2,2,4);image(saltandpe
