Multi-frame blind image deconvolution through split frequency - phase recovery

Adam Gauci; John Abela; Ernest Cachia; Michael Hirsch; Kristian ZarbAdami

doi:10.1117/12.2266257

8 February 2017 Multi-frame blind image deconvolution through split frequency - phase recovery

Adam Gauci, John Abela, Ernest Cachia, Michael Hirsch, Kristian ZarbAdami

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 1022511 (2017) https://doi.org/10.1117/12.2266257
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

Accurate information extraction from images can only be realised if the data is blur free and contains no artificial artefacts. In astronomical images, apart from hardware limitations, biases are introduced by phenomena beyond control such as atmospheric turbulence. The induced blur function does vary in both time and space depending on the astronomical “seeing” conditions as well as the wavelengths being recorded. Multi-frame blind image deconvolution attempts to recover a sharp latent image from an image sequence of blurry and noisy observations without knowledge of the blur applied to each image within the recorded sequence. Finding a solution to this inverse problem that estimates the original scene from convolved data is a heavily ill-posed problem. In this paper we describe a novel multi-frame blind deconvolution algorithm, that performs image restoration by recovering the frequency and phase information of the latent sharp image in two separate steps. For every given image in the sequence a point-spread function (PSF) is estimated that allows iterative refinement of our latent sharp image estimate. The datasets generated for testing purposes assume Moffat or complex Kolmogorov blur kernels. The results from our implemented prototype are promising and encourage further research.

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Adam Gauci, John Abela, Ernest Cachia, Michael Hirsch, and Kristian ZarbAdami "Multi-frame blind image deconvolution through split frequency - phase recovery", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 1022511 (8 February 2017); https://doi.org/10.1117/12.2266257

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KEYWORDS

Point spread functions

Image restoration

Astronomy

Deconvolution

Image deconvolution

Image filtering

Image processing

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