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.
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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_in
% 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
