| import numpy as np | |
| import pywt | |
| import cv2 | |
| from PIL import Image | |
| def wavelet_blocking_noise_estimation(image: Image.Image, blocksize: int = 8) -> Image.Image: | |
| """Estimate local noise using wavelet blocking. Returns a PIL image of the noise map.""" | |
| im = np.array(image.convert('L')) | |
| y = np.double(im) | |
| cA1, (cH, cV, cD) = pywt.dwt2(y, 'db8') | |
| cD = cD[:cD.shape[0] // blocksize * blocksize, :cD.shape[1] // blocksize * blocksize] | |
| block = np.zeros((cD.shape[0] // blocksize, cD.shape[1] // blocksize, blocksize ** 2)) | |
| for ii in range(0, cD.shape[0] - blocksize + 1, blocksize): | |
| for jj in range(0, cD.shape[1] - blocksize + 1, blocksize): | |
| block_elements = cD[ii:ii+blocksize, jj:jj+blocksize] | |
| block[ii // blocksize, jj // blocksize, :] = block_elements.flatten() | |
| noise_map = np.median(np.abs(block), axis=2) / 0.6745 | |
| noise_map_8u = cv2.normalize(noise_map, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U) | |
| resized_noise_map = cv2.resize(noise_map_8u, (im.shape[1], im.shape[0]), interpolation=cv2.INTER_NEAREST) | |
| return Image.fromarray(resized_noise_map) |