In-orbit optical-imaging instruments may suffer from degradations due to space environment impacts or long-time operation. The degradation causes blurring on the image received from the ground. Degradations come from defocus and spherical aberrations cause blurring on the received image. Image deblurring should be done in pre-processing step to compensate the sensor bad impacts. The aberrations are modeled by Zernike polynomials and treated by deep learning in deblurring method. This paper presents a method to deconvolve the acquired data to improve the image quality. A convolution neural network is trained to estimate the point spread function (PSF) parameters using acquired images over satellite calibration site with specific pattern. Image deconvolution is performed to obtain image signal-to-noise (SNR) and modulation transfer function (MTF) improvement. Technical and image data used for modeling and experiment are used from VNREDSat-1 satellite (the first operational Vietnam Earth observation optical small satellite). The experiment is performed on computers accelerated by graphics processing units (GPU) to ensure fast computation.

在轨光学成像仪器可能会受到太空环境的影响或长时间运行而导致性能下降。降级会导致从地面接收到的图像模糊。散焦会导致性能下降，而球面像差会导致接收到的图像模糊。图像去模糊应在预处理步骤中完成，以补偿传感器的不良影响。像差由Zernike多项式建模，并通过去模糊方法的深度学习进行处理。本文提出了一种对数据进行反卷积以提高图像质量的方法。训练卷积神经网络以使用具有特定模式的卫星校准站点上的已采集图像来估计点扩展函数（PSF）参数。执行图像反卷积以获得图像信噪比（SNR）和调制传递函数（MTF）的改进。用于建模和实验的技术和图像数据来自VNREDSat-1卫星（越南第一颗可运行的越南地球观测光学小型卫星）。该实验是在由图形处理单元（GPU）加速的计算机上进行的，以确保快速计算。