Iterative Adaptive Median Filter Implementation

This code implements an iterative adaptive median filter designed to effectively handle salt-and-pepper noise in digital images while preserving important image details and edge information. The median filter is a fundamental image processing technique that operates by sorting pixel values within a defined neighborhood and selecting the median value as the output, effectively reducing various types of image noise. The iterative adaptive median filter enhances traditional median filtering by dynamically adjusting neighborhood sizes and median selection criteria through multiple iterations, allowing it to adapt to varying noise intensities and image characteristics. The implementation employs an optimized algorithm that maintains computational efficiency even when processing large-scale images, utilizing techniques such as sliding window operations and efficient sorting algorithms to minimize processing time. Key functions include adaptive window sizing, iterative noise detection, and dynamic threshold adjustment to balance noise removal with detail preservation. By using this code, users can efficiently apply iterative adaptive median filtering to enhance image quality and mitigate the effects of noise contamination in various computer vision and image processing applications.