Motion Deblurring for Space-Based Imaging on Sandroid CubeSats Using Improved Genetic Algorithm
Xiaoqiang Wu, Fengge Wu, Junsuo Zhao · Procedia Computer Science · 2016
NanoSats have become viable alternative to larger spacecraft that focuses on providing the end user with access to space and similar functionality to mainstream missions. However, motion blur ruins the images captured under the situation that NanoSats work in low-earth orbit at high speeds. In this paper, we address the problem of deblurring images degraded due to space-based imaging system shaking or movements of observing targets. We propose a motion deblurring strategy relying on the powerful on-board computing capability of our Sandroid CubeSats, a member of NanoSats, to compensate for the functional inadequacy of hardware. We use Improved Genetic Algorithm within the strategy to obtain a better linear motion blur kernel and then perform non-blind deconvolution on a single image taken by the space-based imaging system on our Sandroid CubeSats to produce a deblurred result. Experimental results demonstrate the effectiveness of proposed strategy.