Image deconvolution for optical small satellite with deep learning and real-time GPU acceleration

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.