A blind deblurring and image decomposition approach for astronomical image restoration

• Published in: 2015 23rd European Signal Processing Conference (EUSIPCO) pp. 1636 - 1640
• Main Authors: Mourya, Rahul, Denis, Loic, Becker, Jean-Marie, Thiebaut, Eric
• Format: Conference Proceeding
• Language: English
• Published: EURASIP 01-08-2015
• Subjects: ADMM; alternate minimization; Astronomical imaging; blind image deblurring; Estimation; Europe; Huber function; Image restoration; Imaging; non-convex optimization; Optimization; Signal processing; Signal processing algorithms
• ISSN: 2076-1465
• DOI: 10.1109/EUSIPCO.2015.7362661

With the progress of adaptive optics systems, ground-based telescopes acquire images with improved resolutions. However, compensation for atmospheric turbulence is still partial, which leaves good scope for digital restoration techniques to recover fine details in the images. A blind image deblurring algorithm for a single long-exposure image is proposed, which is an instance of maximum-a-posteriori estimation posed as constrained non-convex optimization problem. A view of sky contains mainly two types of sources: point-like and smooth extended sources. The algorithm takes into account this fact explicitly by imposing different priors on these components, and recovers two separate maps for them. Moreover, an appropriate prior on the blur kernel is also considered. The resulting optimization problem is solved by alternating minimization. The initial experimental results on synthetically corrupted images are promising, the algorithm is able to restore the fine details in the image, and recover the point spread function.