Max Denoise !!hot!! -

# 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

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

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

# 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) return 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]

import numpy as np import cv2 import pywt from skimage.restoration import denoise_nl_means, denoise_bilateral from skimage.util import random_noise def max_denoise(image, sigma=0.1, h=1.15, wavelet='db8'): """ Apply maximum-strength denoising using a cascade of methods.

return np.clip(denoised, 0, 1) if name == " main ": import matplotlib.pyplot as plt from skimage import data, img_as_float