Yiğit Arısoy 21727996
HACETTEPE UNIVERSITY
DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING
ELE492 - Special Topics II
Homework #2
1)
Since the pattern in the image is a small hexagon like shape that repeats itself throughout the image, my
first thought to separate it from the rest of the image was to look at the Fourier transform of the image
to see if I could locate the pattern.
I separated the RGB channels into three different arrays and performed the Fourier transform and
graphed it.
The frequency graph helped to isolate the repeating pattern and eliminate it. I removed the 6 dense
positions in the transformed array and reverted back using the inverse transform. A zoomed in
comparison of the original and improved version is shown below.
Yiğit Arısoy 21727996
2)
My first approach to finding the lost image was to enhance the edges in the image. I used various filters
to accomplish this, like the Roberts, sobel and canny. However I wasn’t able to accomplish anything with
this method.
My second approach was to experiment with the exposure properties of the image. I tried to fine tune
the image to find something with the gamma settings and the histogram equalization using skimage
functions. I combined this with edge detectors and different parameters but it was not of much use.
While I progressed, I didn’t come to any meaningful result. The lack of any visual clues in the original
image led me to think that I might be able find something by doing these operations after inverting the
original image instead.
Inverted Image
Yiğit Arısoy 21727996
With this I was able to find the image of what resembles an old man.
The resulting image in different color formats
Yiğit Arısoy 21727996
import numpy as np
import matplotlib.pyplot as plt
import random
from skimage.io import imread,imsave
old = imread("old.jpg")
print(old.shape)a
r_values = old[:, :, 0]
g_values = old[:, :, 1]
b_values = old[:, :, 2]
f1 = np.fft.fft2(r_values)
fshift1 = np.fft.fftshift(f1)
phase_spectrum_r = np.angle(fshift1)
magnitude_spectrum_r = 20*np.log(np.abs(fshift1))
f2 = np.fft.fft2(g_values)
fshift2 = np.fft.fftshift(f2)
phase_spectrum_g = np.angle(fshift2)
magnitude_spectrum_g = 20*np.log(np.abs(fshift2))
f3 = np.fft.fft2(b_values)
fshift3 = np.fft.fftshift(f3)
phase_spectrum_b = np.angle(fshift3)
magnitude_spectrum_b = 20*np.log(np.abs(fshift3))
plt.subplot(231)
plt.imshow(magnitude_spectrum_r)
plt.subplot(232)
plt.imshow(magnitude_spectrum_g)
plt.subplot(233)
plt.imshow(magnitude_spectrum_b)
fshift1[42:90, 125:155] = 1
fshift2[42:90, 125:155] = 1
fshift3[42:90, 125:155] = 1
fshift1[500:548, 125:155] = 1
Yiğit Arısoy 21727996
fshift2[500:548, 125:155] = 1
fshift3[500:548, 125:155] = 1
fshift1[500:548, 305:335] = 1
fshift2[500:548, 305:335] = 1
fshift3[500:548, 305:335] = 1
fshift1[42:90, 305:335] = 1
fshift2[42:90, 305:335] = 1
fshift3[42:90, 305:335] = 1
fshift1[290:310, 400:420] = 1
fshift2[290:310, 400:420] = 1
fshift3[290:310, 400:420] = 1
fshift1[270:330, 20:70] = 1
fshift2[270:330, 20:70] = 1
fshift3[270:330, 20:70] = 1
magnitude_spectrum_r = 20*np.log(np.abs(fshift1))
magnitude_spectrum_g = 20*np.log(np.abs(fshift2))
magnitude_spectrum_b = 20*np.log(np.abs(fshift3))
new_r = abs(np.fft.ifft2(fshift1))
new_g = abs(np.fft.ifft2(fshift2))
new_b = abs(np.fft.ifft2(fshift3))
new_r = new_r.clip(0,255)
new_g = new_g.clip(0,255)
new_b = new_b.clip(0,255)
newPhoto = np.dstack([new_r.astype(int),
new_g.astype(int),
new_b.astype(int)])
imsave("newImproved.jpg",newPhoto)
Yiğit Arısoy 21727996
import numpy as np
import matplotlib.pyplot as plt
import random
from skimage.io import imread,imsave
from skimage import filters,exposure,feature,measure,util
from skimage import color
img = imread("findit.jpg")
imgGamma = exposure.adjust_gamma(img,3)
imgInverted = util.invert(img)
img_eq = exposure.equalize_hist(imgInverted)
imgInvertedGamma = exposure.adjust_gamma(img_eq,5)
image_minmax_scaled = exposure.rescale_intensity(imgInvertedGamma,in_range=(0, 255))
plt.subplot(111)
plt.imshow(image_minmax_scaled.mean(axis=2),cmap="PuOr")
plt.savefig("found.jpg")
img = imread("found.png")
imgGamma = exposure.adjust_gamma(img,2)
plt.subplot(111)
plt.imshow(imgGamma.mean(axis=2),cmap="PuOr")
plt.show()