Re-scaling images using a SVD-based approach

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There are many instances in computer science where computational operations must be performed on matrices of different sizes. In the field of machine learning, particularly when interpreting images, it is often necessary to resize and re-scale images to achieve higher resolutions. While there are various methods for this, they typically involve fitting a simple linear or cubic model to scale the image. The singular value decomposition (SVD) is a powerful tool for dimension reduction and projection. Our proposed state-of-the-art method leverages the capabilities of SVD to create a technique for re-scaling images. This method is primarily based on modifications of the eigenvectors derived from SVD. Previous work has shown that by editing only these Eigenvectors, it is possible to minimize error propagation through the images. When applied to several well-known image processing tasks, it is possible to scale an image with reduced error compared to the current state-of-the-art methods. Additionally, we show that our method can improve the results of machine learning approaches.