Kernel Photo Repair Crack -

def kernel_photo_repair(image, crack_mask): # Define kernel functions def gaussian_kernel(x, y, sigma=1.0): return np.exp(-np.linalg.norm(x - y) ** 2 / (2 * sigma ** 2))

import numpy as np from sklearn.kernel_ridge import KernelRidge from sklearn.metrics import mean_squared_error kernel photo repair crack

# Preprocess image image = np.float32(image) / 255.0 kernel photo repair crack

def laplacian_kernel(x, y, sigma=1.0): return -np.exp(-np.linalg.norm(x - y) ** 2 / (2 * sigma ** 2)) kernel photo repair crack