To achieve high-quality imaging in low-light conditions, a remote sensing camera usually adopts a dynamic time-delay-integration imaging approach, which requires an accurate matching relationship between the optical image field motion and the photo-induced charge transfer. High-frequency motion aberrations still exist due to the measurement frequency limitation of physical attitude and position measurement sensors. However, conventional inversion imaging methods, such as blind deconvolution, can only measure and remove low-frequency motion aberrations. Here, an efficient dynamic inversion imaging algorithm based on double deep learning networks is proposed, which is able to measure and remove high-frequency motion aberrations. To measure high-frequency motion aberrations, we constructed two supervised online deep learning networks, a high-frequency motion aberration inversion learning network (HMAILN) and an optical flow inversion learning network (OFILN). The OFILN can measure the accurate optical flow information, which forms the input training set of the HMAILN. The HMAILN completes the measurement of high-frequency motion aberrations. Finally, the measured high-frequency motion aberrations from the HMAILN were used to construct the motion point spread function for imaging compensation to remove high-frequency motion aberrations. The proposed method was experimentally confirmed, opening the door for the successful implementation of dynamic high-resolution imaging without high-frequency motion aberrations.

为了在弱光条件下实现高质量的成像，遥感摄像机通常采用动态时滞积分成像方法，这需要光学像场运动与光感应电荷转移之间具有精确的匹配关系。由于身体姿势和位置测量传感器的测量频率限制，高频运动像差仍然存在。但是，常规的反演成像方法（例如盲反卷积）只能测量和消除低频运动像差。在此，提出了一种基于双深度学习网络的高效动态反演成像算法，该算法能够测量和消除高频运动像差。为了测量高频运动像差，我们构建了两个受监管的在线深度学习网络，高频运动像差反转学习网络（HMAILN）和光流反转学习网络（OFILN）。OFILN可以测量准确的光流信息，从而形成HMAILN的输入训练集。HMAILN完成了高频运动像差的测量。最后，从HMAILN测得的高频运动像差用于构造运动点扩展函数，以进行成像补偿以消除高频运动像差。实验证明了该方法的成功，为成功实现动态高分辨率图像而没有高频运动像差打开了大门。OFILN可以测量准确的光流信息，从而形成HMAILN的输入训练集。HMAILN完成了高频运动像差的测量。最后，从HMAILN测得的高频运动像差用于构造运动点扩展函数，以进行成像补偿以消除高频运动像差。实验证明了该方法的成功，为成功实现动态高分辨率成像而没有高频运动像差打开了大门。OFILN可以测量准确的光流信息，从而形成HMAILN的输入训练集。HMAILN完成了高频运动像差的测量。最后，从HMAILN测得的高频运动像差用于构造运动点扩展函数，以进行成像补偿以消除高频运动像差。实验证明了该方法的成功，为成功实现动态高分辨率成像而没有高频运动像差打开了大门。从HMAILN测得的高频运动像差用于构造运动点扩展函数，以进行成像补偿以消除高频运动像差。实验证明了该方法的成功，为成功实现动态高分辨率图像而没有高频运动像差打开了大门。从HMAILN测得的高频运动像差用于构造运动点扩展函数，以进行成像补偿以消除高频运动像差。实验证明了该方法的成功，为成功实现动态高分辨率图像而没有高频运动像差打开了大门。