MATLAB Implementation of SVD (Singular Value Decomposition) for Image Processing

The SVD program is a fundamental image processing technique that performs singular value decomposition on images. SVD is a mathematical method that decomposes a matrix into three constituent matrices - U, S, and V - enabling extraction of essential feature information from images. This implementation can be applied to various image processing tasks including image compression, enhancement, and reconstruction. Through MATLAB's svd() function, we can efficiently compute the decomposition where U contains left singular vectors, S is a diagonal matrix of singular values, and V contains right singular vectors. By retaining only the most significant singular values (typically using thresholding or percentage-based selection), we achieve effective image compression while maintaining visual quality. The program demonstrates practical implementation of dimensionality reduction techniques, allowing for better understanding and manipulation of image data to improve overall image quality and visual outcomes.