# Compute universal threshold threshold = sigma * np.sqrt(2 * np.log(denoised.size))
# 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) max denoise
return np.clip(denoised, 0, 1) if name == " main ": import matplotlib.pyplot as plt from skimage import data, img_as_float # Compute universal threshold threshold = sigma * np
# Apply hard thresholding to detail coefficients def threshold_coeffs(coeff_list, thr): return [pywt.threshold(c, thr, mode='hard') for c in coeff_list] level=4) return np.clip(denoised
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)