Fast model of space-variant blurring and its application to deconvolution in astronomy

• Published in: 2011 18th IEEE International Conference on Image Processing pp. 2817 - 2820
• Main Authors: Denis, L., Thiebaut, E., Soulez, F.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2011
• Subjects: Computational modeling; Convolution; Deconvolution; Image restoration; Interpolation; Kernel; shift-variant PSF
• ISBN: 1457713047; 9781457713040
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2011.6116257

Image deblurring is essential to high resolution imaging and is therefore widely used in astronomy, microscopy or computational photography. While shift-invariant blur is modeled by convolution and leads to fast FFT-based algorithms, shift-variant blurring requires models both accurate and fast. When the point spread function (PSF) varies smoothly across the field, these two opposite objectives can be reached by interpolating from a grid of PSF samples. Several models for smoothly varying PSF co-exist in the literature. We advocate that one of them is both physically-grounded and fast. Moreover, we show that the approximation can be largely improved by tuning the PSF samples and interpolation weights with respect to a given continuous model. This improvement comes without increasing the computational cost of the blurring operator. We illustrate the developed blurring model on a deconvo-lution application in astronomy. Regularized reconstruction with our model leads to large improvements over existing results.