Can you provide the magnitude spectrum test code of 2D Fast Fourier Transform (FFT) mentioned in the paper about pisr HOT 9 CLOSED

Thank you very much for your timely reply, which is very helpful to me!

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Hello, thanks for your attention.
Actually, the codes for obtaining high-freq signals are not organized well because we implemented it in the jupyter-notebook file.
Instead of sharing the full code, I gonna provide you the core functions.

def get_spectrum(img):
    f = np.fft.fft2(img)
    fshift = np.fft.fftshift(f)
    magnitude_spectrum = 20*np.log(np.abs(fshift))
    return magnitude_spectrum.squeeze()

def plot_spectrum(lr_upsampled, hr, encoded_lr_upsampled, notim_encoded_lr_upsampled):
    magnitude_spectrum_hr = get_spectrum(hr)
    magnitude_spectrum_lr = get_spectrum(lr_upsampled)
    magnitude_spectrum_encoded_lr = get_spectrum(encoded_lr_upsampled)
    magnitude_spectrum_notim = get_spectrum(notim_encoded_lr_upsampled)
    
    # skip detailed codes. #
    plt.plot(magnitude_spectrum_lr[h, w-m:w+m])
    plt.plot(magnitude_spectrum_encoded_lr[h, w-m:w+m])
    plt.plot(magnitude_spectrum_notim[h, w-m:w+m])
    plt.show()

I hope those are helpful.

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I have some problems that I hope you can solve.

1. In "magnitude_spectrum_hr[h,w - m:w + m]", h and w should be the height and width of the input image, but I don't understand what "m" means.

2. I see that all the low resolution (lr) images in the code are suffixed with "_upsampled", which means that the results obtained in Fig. 3 in the paper are compared after being upsampled on the low resolution images? If so, which upsampling method is used.

Looking forward to your reply again.

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1. First of all, the input of the function is a cropped image whose size is 200 by 200 (for the HR case).
   As you know, the output of the function get_spectrum() has the same shape of the image, which is 200 by 200 in this case.
   For plotting the magnitude spectrum like the left-bottom of Figure 3, we only need the 100th row (i.e., v=0 results from the fft-shift) of the output, therefore we set h to 100.
   Also, we set w and m to 100 and 50, respectively, I'll explain the reason in the next answer.

2. If we'd like to plot both magnitude spectrums from HR and LR into one figure, we need to calibrate the frequency axis of them because the shapes of each spectrum are (200, 200) and (100, 100), respectively. Therefore, we upsampled LR and encoded LR images by two times with a bilinear interpolation method.
   After the upsampling, the magnitude components at u > 150 and u < 50 are not originated from the LR (and encoded LR) images. This led us to set w and m to 100 and 50.

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Thank you for your kind and attentive reply!

Since I am not solid enough in Fourier transform, I may make mistakes when testing my images. Here are a few questions. If the size of my high-resolution (hr) image is 360x256 and the size of my low-resolution (lr) image is 45x32, and the lr image is upsampled by eight times with a bilinear interpolation. Can the values of h, w and m be set to 180, 180 and 64 respectively?

Looking forward to your reply, thanks again!

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This figure shows the results of HR and LR in the previous comment. The red line is HR and the blue line is LR

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I think m should be 16 (= 256 / 8 / 2, which has the same relationship with 200 / 2 / 2 of our case).

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OK, I get it.
Thank you.

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Hello!
Have you run the author's code?The author's code, it doesn't work
Have you made any changes to the author's code?

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