The synthetic aperture technology can improve the resolution effectively in the optical imaging system. In fact, the imaging blur, turbulence aberration and noise can affect the imaging quality of optical synthetic aperture imaging system seriously. Several non-blind methods are applied generally to recover the degraded maps with the prior information. However, the restoration effect is not stable enough and satisfactory. As a data-driven approach, the deep learning framework possesses advantages in solving this problem. In this paper we propose an improved network, RestoreNet-Plus, for the image restoration of optical synthetic aperture imaging system. After the proofs of numerical simulation and experiment results, RestoreNet-Plus is a better alternative compared with other methods, owing to its better restoration ability, strong denoising ability and capacity for turbulence correction error.

合成孔径技术可以有效提高光学成像系统的分辨率。事实上，成像模糊、湍流像差和噪声会严重影响光学合成孔径成像系统的成像质量。通常应用几种非盲方法来恢复具有先验信息的退化地图。然而，修复效果不够稳定和令人满意。作为一种数据驱动的方法，深度学习框架在解决这个问题上具有优势。在本文中，我们提出了一种改进的网络，RestoreNet-Plus，用于光学合成孔径成像系统的图像恢复。经过数值模拟和实验结果证明，RestoreNet-Plus 与其他方法相比是更好的替代方案，因为它具有更好的恢复能力，