The result of transforms.Lambda(extract_single) is numpy file with one channel, while images are three channels.
I have already obtained the normalized numbers for the images. But it is not applicable to one channel. Now I don’t know which number of [0.4786, 0.4728, 0.4528], [0.2425, 0.2327, 0.2564] should be used or I need to calculate another mean & std for the one channel?

This might be a valid approach, but it would also be interesting to know what exactly extract_single does as you might be able to reuse the same approach for the stats.
E.g. if extract_single slices the tensor in a single channel you could use the corresponding value from the mean and std stats, too.

extract_single function extracts prnu noise from images:

def extract_single(im: np.ndarray,
levels: int = 4,
sigma: float = 5,
wdft_sigma: float = 0) -> np.ndarray:
"""
Extract noise residual from a single image
:param im: grayscale or color image, np.uint8
:param levels: number of wavelet decomposition levels
:param sigma: estimated noise power
:param wdft_sigma: estimated DFT noise power
:return: noise residual
"""
W = noise_extract(im, levels, sigma)
W = rgb2gray(W)
W = zero_mean_total(W)
W_std = W.std(ddof=1) if wdft_sigma == 0 else wdft_sigma
W = wiener_dft(W, W_std).astype(np.float32)
return W

It works, but I think this way causes increasing processing because of 2 times ToTensor() and because of the addition of ToPILImage(). Is there a better way?