Tamás Budavári receives NSF Grant to Develop Image Processing Strategies for Repeated Observations

Dr. Tamás Budavári, an IDIES member in the Department of Applied Mathematics and Statistics, has received an NSF Astronomy and Astrophysics Research Grant (AAG) to develop new image processing strategies for repeated observations. His work with graduate student Matthias Lee promises to provide never-before-seen high-resolution photos and high-quality measurements for legacy datasets as well as next-generation time-domain surveys.

Left: Four sample images from Sloan’s Stripe 82;
Right: Our Multi Frame Blind Deconvolution result using 70 input frames.
Newly discovered sources are circled in red.

“Our approach allows us to extract information from multiple noisy images to create a single higher quality image. It allows us to reliably sharpens existing sources and expose new sources that previously were too faint to see.”

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