Description
For this problem, handin Matlab .m files for the functions described by the headers below. Note that one of these is a driver which creates inputs for each function and runs the function on those inputs to obtain the output. Also, you are to turn in a document (A3.pdf) which includes a number of figures to demonstrate your Matlab functions. You are to use two images: im and trees found in the code/A3 subdir on my class webpage. The document should be organized as follows: • Section 1: Power Spectrum: Show 2 rows each with 3 images: the original image, imshow(mat2gray()) and surf() of the test images. • Section 2: Ideal Low Pass Filter: give 2 rows each for im and trees where the first row uses imshow to demonstrate the spatial filtering result (7×7 block), frequency filtering result, and combo of those, and row 2 uses surf to show the same 3 things. • Section 3: Gaussian Low Pass Filter: give 2 rows each for im and trees where the first row uses imshow to demonstrate the spatial filtering result (7×7 block), frequency filtering result, and combo of those, and row 2 uses surf to show the same 3 things. • Section 4: High Pass LoG Filter: give 2 rows each for im and trees where the first row uses imshow to demonstrate the spatial filtering result (7×7 block), frequency filtering result, and combo of those, and row 2 uses surf to show the same 3 things. • Section 5: Sharpening: use the Gaussian low pass image and add the LoG filtered image to sharpen the image. Give 2 rows each for im and trees where the first row uses imshow to demonstrate the blurred image, the LoG image, and the sharpened result, and row 2 uses surf to show the same 3 things. 1 Some notes: • Indent headers correctly (5 spaces indented lines) • Do not exceed 72 characters per source line • CS4640 A3 driver: should show that each function works None of the functions should write to the interpreter, draw, etc. function imc = CS4640_center(im) % CS4640_center – center image for Fourier transform % On input: % im (MxN array): input image % On output: % imc (MxN array): centered image % Call: % imc = CS4640_center(im); % Author: % % UU % Spring 2018 % function imP = CS4640_power_spectrum(im,centered) % CS4640_power_spectrum – power spectrum of FT of image % On input: % im (MxN double array): input image % centered (Boolean): if 1 centered, else not % On output: % imP (MxN double array): power spectrum % Call: % imP = CS4640_power_spectrum(im,1); % Author: % % UU % Spring 2018 % 2 function im_pad = CS4640_pad(im,P,Q) % CS4640_im_pad – pad an image for frequency domain filtering % On input: % im_in (MxN double array): input image % On output: % im_pad (PxQ double array): upper MxN is im; rest is 0 % Call: % imp = CS4640_pad(im,2*M,2*N); % Author: % % UU % Spring 2018 % function im_depad = CS4640_depad(im,M,N) % CS4640_im_depad – depad an image for frequency domain filtering % On input: % im (PxQ double array): padded input image % On output: % im_depad (MxN double array): upper MxN of im % Call: % imdp = CS4640_depad(im,M,N); % Author: % % UU % Spring 2018 % function g = CS4640_spatial_filter(im_in,H) % CS4640_spatial_filter – spatial domain filtering % On input: % im_in (MxN double array): input image % H (kxk double array, k odd): filter % On output: % g (MxN double array): filtered image; i.e., im_inH % Call: % im_lp = CS4640_spatial_filter(im,ones(7,7)/49); % Author: % 3 % UU % Spring 2018 % function g = CS4640_freq_filter(im_in,H) % CS4640_freq_filter – frequency domain filtering % On input: % im_in (MxN double array): input image % H (kxk double array, k odd): filter % On output: % g (MxN double array): filtered image; i.e., % Fˆ{-1}[F(im_in).*F(H)] % Call: % im_lp = CS4640_freq_filter(im,ones(7,7)/49); % Author: % % UU % Spring 2018 % function CS4640_A3_driver % CS4640_A3_driver – driver for A3 functions % On input: % N/A % On output: % N/A % Call: % CS4640_A3_driver % Author: % % UU % Spring 2018 % 4
