1/f whitening for large natural images

f_whitening.py

#PCA whitening involves finding the inverse square root of the covariance matrix 
#of a set of observations, which is prohibitively expensive when dealing 
#with natural images 

#starting with a path to a single image (img_path)
import numpy as np
from PIL import Image
from sklearn import preprocessing


def f_whitening(img_path):
	img = Image.open(open(img_path))
	img = np.asarray(img, dtype='uint8').transpose(2, 0, 1)
	whitened = []
	for ii in range(3):#loop over color channels
		ch = img[ii] - img[ii].mean()#center the data
		aa = np.fft.fft2(a)
		spectr = np.sqrt(np.mean(np.dot(abs(aa),abs(aa))))
		out = np.fft.ifft2(np.dot(aa,1./spectr))
		whitened.append(preprocessing.scale(abs(out)))
	return np.asarray(whitened)

Thanks for this gist!
I spotted a small error line 17, it should be aa = np.fft.fft2(ch)
Also, do you have any ressource that could explain your code ?