Singular Value Decomposition of Images from Scanned Photographic Plates
This work addresses storage and processing challenges for astronomical data archives, but it is incremental as it applies an existing method (SVD) to a new type of data (scanned photographic plates).
The researchers tackled the problem of compressing scanned astronomical photographic plates by using Singular Value Decomposition (SVD) to approximate images with fewer entries, achieving over 98% compression ratio while preserving image details.
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.