Max Denoise Page

# 3. Strong Bilateral filter (smooths while keeping edges) denoised = denoise_bilateral(denoised, sigma_color=0.3, sigma_spatial=5, multichannel=(image.ndim==3))

# Apply hard thresholding to detail coefficients def threshold_coeffs(coeff_list, thr): return [pywt.threshold(c, thr, mode='hard') for c in coeff_list] max denoise

# 4. Median filter (removes any remaining salt-and-pepper noise) denoised = cv2.medianBlur((denoised * 255).astype(np.uint8), 3).astype(np.float32) / 255.0 Strong Non-Local Means (preserves edges while smoothing flat

if denoised.ndim == 2: coeffs = list(coeffs) coeffs[1:] = threshold_coeffs(coeffs[1:], threshold) denoised = pywt.waverec2(coeffs, wavelet) else: coeffs = list(coeffs) coeffs[1:] = threshold_coeffs(coeffs[1:], threshold) denoised = pywt.waverec(coeffs, wavelet) var=0.04) noisy = random_noise(noisy

# 1. Strong Non-Local Means (preserves edges while smoothing flat regions) denoised = denoise_nl_means(image, h=h, sigma=sigma, fast_mode=True, patch_size=7, patch_distance=11, multichannel=(image.ndim==3))

# 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

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