((free)) - Max Denoise

# Display fig, axes = plt.subplots(1, 3, figsize=(12, 4)) axes[0].imshow(original, cmap='gray') axes[0].set_title('Original') axes[1].imshow(noisy, cmap='gray') axes[1].set_title('Noisy') axes[2].imshow(denoised, cmap='gray') axes[2].set_title('Max Denoised') for ax in axes: ax.axis('off') plt.tight_layout() plt.show()

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. max denoise

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) # Display fig, axes = plt

# Load a test image original = img_as_float(data.camera()) # Display fig