Introduction to Python OpenCV: Denoising Methods in Image Preprocessing

In image preprocessing, denoising is a core step to eliminate noise (such as Gaussian, salt-and-pepper, Poisson noise) during acquisition/transmission and improve the accuracy of subsequent tasks. Python OpenCV provides multiple denoising methods: 1. **Mean Filtering**: A simple average of window pixels, fast but blurs edges. Suitable for Gaussian noise, implemented with `cv2.blur` (3×3 kernel). 2. **Median Filtering**: Replaces the center pixel with the median of window pixels. Effective against salt-and-pepper noise (0/255 specks), preserves edges well. Kernel size must be odd (e.g., 3×3), using `cv2.medianBlur`. 3. **Gaussian Filtering**: Weighted average using a Gaussian distribution kernel, balances denoising and edge preservation. Ideal for Gaussian noise, requires kernel size and standard deviation in `cv2.GaussianBlur`. 4. **Bilateral Filtering**: Combines spatial and color distance, excels at edge-preserving denoising with high computational cost. Suitable for high-precision scenarios (e.g., face images), implemented with `cv2.bilateralFilter`. **Selection Guidelines**: Gaussian noise → Gaussian filtering; salt-and-pepper noise → median filtering; mixed noise → Gaussian followed by median; high-frequency detail noise → bilateral filtering. Beginners are advised to start with Gaussian and median filters, adjusting based on... *(Note: The original text ends abruptly; the translation concludes at the logical cutoff point.)*