A New Method for Band-limited Imaging with Undersampled Detectors
Andrew S. Fruchter

TL;DR
The paper introduces iDrizzle, an iterative method that improves band-limited image reconstruction from undersampled data, reducing artifacts and handling distortions, with applications in high-precision astrophysical measurements.
Contribution
It presents iDrizzle, a novel iterative algorithm that enhances image quality from undersampled data, outperforming traditional drizzling especially for high-precision applications.
Findings
Effectively reduces high-frequency artifacts.
Handles geometric distortion and missing data.
Suitable for high-fidelity PSF reconstruction.
Abstract
Since its original use on the Hubble Deep Field, "Drizzle" has become a de facto standard for the combination of images taken by the Hubble Space Tele- scope. However, the drizzle algorithm was developed with small, faint, partially resolved sources in mind, and is not the best possible algorithm for high signal-to-noise unresolved objects. Here, a new method for creating band-limited images from undersampled data is presented. The method uses a drizzled image as a first order approximation and then rapidly converges toward a band-limited image which fits the data given the statistical weighting provided by the drizzled image. The method, named iDrizzle, for iterative Drizzle, effectively eliminates both the small high-frequency artifacts and convolution with an interpolant kernel that can be introduced by drizzling. The method works well in the presence of geometric distortion, and can…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
