This project is a case study on applying Singular Value Decomposition (SVD) for image reconstruction and low-rank approximation.
It demonstrates how a grayscale image can be decomposed and reconstructed using only a few dominant singular values, connecting linear algebra theory to a tangible visual result.
The example image used in this project is a grayscale photograph of a rose from Pexels.
This image serves as a concrete case study to illustrate the beauty of mathematics in data science and image processing.
