# 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
# 3. Strong Bilateral filter (smooths while keeping edges) denoised = denoise_bilateral(denoised, sigma_color=0.3, sigma_spatial=5, multichannel=(image.ndim==3)) 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() # Add strong synthetic noise noisy = random_noise(original,
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 var=0.04) noisy = random_noise(noisy