Image Denoising Techniques and Implementation

Image Denoising: This process involves adding different types of noise (such as random pixel noise, salt-and-pepper noise) to an image and applying various denoising methods like neighborhood averaging, median filtering, and image stacking. Implementation typically involves using Python's OpenCV library or MATLAB's image processing toolbox, where functions like cv2.blur() for neighborhood averaging and cv2.medianBlur() for median filtering can be applied with specific kernel sizes. The comparison of different methods against various noise patterns helps determine optimal approaches - for instance, median filtering excels at removing salt-and-pepper noise while preserving edges better than linear filters. Further analysis should explore each method's advantages and limitations: neighborhood averaging is computationally efficient but may cause blurring, while median filtering handles impulse noise effectively but might struggle with Gaussian noise. Considerations for selecting appropriate denoising techniques include noise characteristics, computational complexity, and edge preservation requirements, providing comprehensive understanding for practical image denoising applications.