The file SVD_on_images.ipynb is a Jupyter Notebook that demonstrates the application of Singular Value Decomposition (SVD) on images. SVD is a matrix factorization technique that can be used for various purposes, including image compression and denoising. In this notebook, you will find:
An explanation of how SVD works and its relevance in image processing.
Practical code examples showing how to use SVD to compress images while preserving important information.
Visualizations and comparisons of original images and SVD-processed images.
Step-by-step instructions on how to run the code and experiment with different images.