Dictionary based encoding of cosmological images

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.