Singular Value Decomposition of Images from Scanned Photographic Plates

TL;DR

This paper explores using Singular Value Decomposition (SVD) to compress astronomical photographic plate images, achieving over 98% reduction in data size while preserving essential image details.

Contribution

It demonstrates a method for image compression of scanned astronomical plates using SVD, enabling significant data reduction with minimal loss of important information.

Findings

Achieved over 98% image compression ratio.

Effectively removed noise and redundant information.

Maintained image details with low-rank approximation.

Abstract

We want to approximate the mxn image A from scanned astronomical photographic plates (from the Sofia Sky Archive Data Center) by using far fewer entries than in the original matrix. By using rank of a matrix, k we remove the redundant information or noise and use as Wiener filter, when rank k<m or k<n. With this approximation more than 98% compression ration of image of astronomical plate without that image details, is obtained. The SVD of images from scanned photographic plates (SPP) is considered and its possible image compression.