Image Fading Algorithm Based on K-MEANS Clustering

This article presents a K-MEANS-based image fading algorithm, a practical image processing technique for color manipulation. While currently implemented for MATLAB 6.5 and higher platforms, the algorithm demonstrates significant potential for broader applications in future research. The implementation leverages K-means clustering to partition image colors into predefined groups (typically K=2-8 clusters), reducing color complexity while preserving essential image details through centroid recalculation and iterative reassignment. By applying this algorithm, users can effectively process images to enhance color vibrancy through selective color reduction, making it particularly valuable for high-volume image processing scenarios such as digital art creation, film production, and game development. The core MATLAB functions involved include kmeans() for color clustering, imread() for image input, and customized color mapping routines for output generation. As a promising color quantization technology, the K-MEANS-based fading algorithm is expected to play an increasingly important role in future image processing research and applications.