Max Denoise Patched -

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

# Add strong synthetic noise noisy = random_noise(original, mode='gaussian', var=0.04) noisy = random_noise(noisy, mode='s&p', amount=0.05) # extra salt & pepper max denoise

# Apply maximal denoising denoised = max_denoise(noisy, sigma=0.2, h=1.5) # Load a test image original = img_as_float(data

return np.clip(denoised, 0, 1) if name == " main ": import matplotlib.pyplot as plt from skimage import data, img_as_float var=0.04) noisy = random_noise(noisy

# 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()

Parameters: - image: numpy array (grayscale or color) normalized to [0,1] or [0,255] - sigma: estimated noise standard deviation (used for wavelet threshold) - h: non-local means filter strength (larger = stronger denoising) - wavelet: wavelet type for thresholding