Image Reconstruction Using PCA

Principal Component Analysis (PCA) is a powerful technique widely used in image processing, computer vision, and machine learning. It’s a method for reducing the dimensionality of high-dimensional data and has many practical applications. This project aims to delve into the world of dimensionality reduction and image generation using PCA.

This project aims to understand the mathematical concepts behind PCA, including eigenvectors, eigenvalues, and the decomposition of the covariance matrix. The project will also involve implementing PCA from scratch and comparing it to the PCA implementation from the scikit-learn library. The project will further involve reconstructing images using the PCA basis and generating new images by randomly choosing weights for each PCA component.

Additionally, the project will explore the impact of data size and other parameters on the results and compare the results of the PCA-based generator to state-of-the-art image generators. The project will provide a deeper understanding of PCA and its applications in image processing. By the end of this project, you will have a solid foundation in PCA and be able to apply it to a wide range of image processing tasks.