Multi‐frame blind deconvolution of atmospheric turbulence degraded images with mixed noise models

Abstract

This version is available at https://strathprints.strath.ac.uk/62591/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. This letter proposes a mixed noise model and uses the multi-frame blind deconvolution to restore the images of space objects under the Bayesian inference framework. To minimize the cost function, an algorithm based on iterative recursion was proposed. In addition, three limited bandwidth constraints of the point spread functions were imposed into the solution process to avoid converging to local minima. Experimental results show that the proposed algorithm can effectively restore the turbulence degraded images and alleviate the distortion caused by the noise.Introduction: Space object surveillance plays a fundamental and critical role in future space exploration. Images of space objects are usually acquired with ground-based telescopes. The image resolution, however, is limited due to the presence of the atmospheric turbulence (which causes the uneven distribution of the refractive index and leads to the wavefront distortion). This greatly deteriorates the quality and resolution of the images. A powerful approach called multi-frame blind deconvolution (MFBD) can significantly reduce the impact of atmospheric turbulence on an imaging system. MFBD can simultaneously estimate the unblurred object and the point spread functions (PSF) from a set of observed noise-inflicted images. The key step of applying MFBD is to accurately introduce a priori information in the restoration process. Many MFBD algorithms and theoretical results have been developed; they used different a priori information in image restoration. Conventional MFBD algorithms usually assume that the observed images are corrupted by a single type of noise, either Poisson noise [1][2][3] or Gaussian noise [4,5]. Instead of adopting these strategies, we propose a novel multi-frame image restoration algorithm by adopting a mixed noise model (MFRAM); MFRAM can...

Multi-Frame Blind Restoration for Image of Space Target With FRC and Branch-Attention

“…During the course of experiments, we set the ranges of r and λ in (3)(4) to [0.01, 0.015] and [35,42] respectively. Different PSFs and noise are generated randomly from these two ranges and the clean images we selected from STK would go through the process showed in Fig.5 to produce the multi-frame degraded inputs for the network.…”

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