| import numpy as np |
| import pywt |
| import cv2 |
| from PIL import Image |
|
|
| def noise_estimation(image: Image.Image, blocksize: int = 8) -> Image.Image: |
| """Estimate local noise using wavelet blocking. Returns a PIL image of the noise map. |
| |
| Args: |
| image (Image.Image, string: filepath): The input image to analyze. |
| blocksize (int): The size of the blocks to use for wavelet blocking. |
| |
| Returns: |
| Image.Image: 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) |
