Motion Deblurring via Using Generative Adversarial Networks for Space-Based Imaging
Yi Chen, Fengge Wu, Junsuo Zhao · 2018
In some missions of NanoSats, we find images captured are disturbed by motion blur which caused 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 via using Generative Adversarial Networks(GAN) to realize an end-to-end image processing without kernel estimation in orbit. We combine Wasserstein GAN(WGAN) and loss function based on adversarial loss and perceptual loss to optimize the result of deblurred image. The experimental results on the two different datasets prove the feasibility and effectiveness of the proposed strategy which outperforms the state-of-the-art blind deblurring algorithms using for remote sensing images both quantitatively and qualitatively.