Algorithm 1: Transforming IBI spatial images into frequency domain images

Input: IBI spatial image  Data: Image data $Im{g}_{data}$ as array  Output: IBI frequency domain image  /* apply 2-D fast Fourier transform   f← np.fft.fft2($Im{g}_{data}$);   f← np.fft.fftshift(f);   f← np.abs(f);   f← np.${log}_{10}\left(f\right)$;   /* find the original contrast range   high← np.nanmax(fourier[np.isfinite(f)]);   min← np.nanmin(fourier[np.isfinite(f)]);   orgContImg← high-min;   /* transofrm normalized data into an image   normFourier ← (f-min) / orgContImg * 255;   normFourierImg ← Image.fromarray(normFourier);   return normFourierImg;   /* Save image as a file   matplotlib.image.imsave(flname, normFourierImg,cmap = viridis)

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