Max Denoise [work] 🆕 No Sign-up

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

# 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)) max denoise

# 2. Wavelet hard thresholding (removes residual high-frequency noise) coeffs = pywt.wavedec2(denoised, wavelet, level=4) if denoised.ndim == 2 else \ pywt.wavedec(denoised, wavelet, level=4) threshold) denoised = pywt.waverec2(coeffs

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) threshold) denoised = pywt.waverec(coeffs

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

# Compute universal threshold threshold = sigma * np.sqrt(2 * np.log(denoised.size))

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