[SOLVED] Cs 270: digital image processing assignment 1

Please complete all the coding assignments using MATLAB. Make sure your results in the report are the
same as the results of your codes. For general operations, the following functions may be useful:
load, imread, double, im2double, uint8, imshow, zeros, size, montage, subplot, bar.You must implement the core code in each question WITHOUT using relevant build-in functions, e.g,
fspecial, imfilter, filter2, conv2, imsharpen, ordfilt2, hist, imadjust, histeq, adapthisteq,
etc. You can type help FunctionName in Command Window of MATLAB for detailed help text for the
functionality specified by FunctionName.(a) Compute the histogram of grain.tif and show the histogram image in your report. (Built-in
functions hist and histogram are not allowed). (5 points)(b) Implement histogram equalization on grain.tif. Show the histogram equalized image and the
histogram of equalized image in your report. (Built-in function histeq is not allowed). (10 points)
(c) Implement contrast limited adaptive histogram equalization (CLAHE) on tire.tif, where you traverse every pixel with a 41 × 41 patch and process histogram equalization within each patch and
update the center patch of 3 ×3. Each time you move the patch with a step of 1 pixel.The clip limit
for CLAHE is 0.02, which means that after the normalization of the histogram, amplification above
0.02 should be clipped and evenly distributed to other parts of the histogram. Show the CLAHE
processed images and corresponding histogram in your report. (Built-in function adapthisteq is not
allowed) (20 points)(a) Apply the 3 × 3 Laplacian kernels (x-direction and y-direction) on moon.jpg to obtain the details
of the image. Since the Laplacian kernels are separable, please separate them into combinations of 1-D
kernels and then apply them to the image sequentially. Show the separated Laplacian kernels and the
corresponding processed images. (Implement your own convolution operator.) (15 points)(b) Apply the 3 × 3 Laplacian kernels (not separated) to sharpen the image by 2-D convolution. Show
the results in your report. (10 points)
(c) Use unsharpen mask to sharpen the image. Show the results in your report. (10 points)
The pixel intensity of the result images should be normalized to uint8 values within [0, 255].Apply the median filter and Gaussian filter to the image lena noisy.tif. In your report, Please show
the images processed by the median filter and Gaussian filter respectively, and analyze the cause of the
results. (Filtering operations cannot be implemented by calling functions, Hint: Choose the best kernel
size you think, σ = 1) (30 points)