import numpy as np import cv2 import pywt from skimage.restoration import denoise_nl_means, denoise_bilateral from skimage.util import random_noise def max_denoise(image, sigma=0.1, h=1.15, wavelet='db8'): """ Apply maximum-strength denoising using a cascade of methods.
# Load a test image original = img_as_float(data.camera()) max denoise
return np.clip(denoised, 0, 1) if name == " main ": import matplotlib.pyplot as plt from skimage import data, img_as_float import numpy as np import cv2 import pywt from skimage
Returns: - denoised: maximally denoised image """ # Ensure float in [0,1] range if image.max() > 1.0: image = image.astype(np.float32) / 255.0 else: image = image.astype(np.float32) 1] range if image.max() >
# Apply maximal denoising denoised = max_denoise(noisy, sigma=0.2, h=1.5)
# Apply hard thresholding to detail coefficients def threshold_coeffs(coeff_list, thr): return [pywt.threshold(c, thr, mode='hard') for c in coeff_list]