# Apply hard thresholding to detail coefficients def threshold_coeffs(coeff_list, thr): return [pywt.threshold(c, thr, mode='hard') for c in coeff_list]
# 4. Median filter (removes any remaining salt-and-pepper noise) denoised = cv2.medianBlur((denoised * 255).astype(np.uint8), 3).astype(np.float32) / 255.0 max denoise
return np.clip(denoised, 0, 1) if name == " main ": import matplotlib.pyplot as plt from skimage import data, img_as_float # Apply hard thresholding to detail coefficients def
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) thr): return [pywt.threshold(c
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)
# Apply hard thresholding to detail coefficients def threshold_coeffs(coeff_list, thr): return [pywt.threshold(c, thr, mode='hard') for c in coeff_list]
# 4. Median filter (removes any remaining salt-and-pepper noise) denoised = cv2.medianBlur((denoised * 255).astype(np.uint8), 3).astype(np.float32) / 255.0
return np.clip(denoised, 0, 1) if name == " main ": import matplotlib.pyplot as plt from skimage import data, img_as_float
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)
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)