Ground-based solar image restoration is a computationally expensive procedure that involves nonlinear optimization techniques. The presence of atmospheric turbulence produces perturbations in individual images that make it necessary to apply blind deconvolution techniques. These techniques rely on the observation of many short exposure frames that are used to simultaneously infer the instantaneous state of the atmosphere and the unperturbed object. We have recently explored the use of machine learning to accelerate this process, with promising results. We build upon this previous work to propose several interesting improvements that lead to better models. As well, we propose a new method to accelerate the restoration based on algorithm unrolling. In this method, the image restoration problem is solved with a gradient descent method that is unrolled and accelerated aided by a few small neural networks. The role of the neural networks is to correct the estimation of the solution at each iterative step. The model is trained to perform the optimization in a small fixed number of steps with a curated dataset. Our findings demonstrate that both methods significantly reduce the restoration time compared to the standard optimization procedure. Furthermore, we showcase that these models can be trained in an unsupervised manner using observed images from three different instruments. Remarkably, they also exhibit robust generalization capabilities when applied to new datasets. To foster further research and collaboration, we openly provide the trained models, along with the corresponding training and evaluation code, as well as the training dataset, to the scientific community.

翻译：地基太阳图像复原是一个计算成本高昂的过程，涉及非线性优化技术。大气湍流的存在会导致单幅图像产生扰动，因此必须应用盲反卷积技术。这些技术依赖于观测大量短曝光帧，用于同时推断大气的瞬时状态和未受扰动的目标。我们近期探索了利用机器学习加速该过程的可行性，并取得了令人期待的结果。本研究在前述工作基础上提出了若干有意义的改进方案，从而获得了更优的模型。此外，我们提出了一种基于算法展开的新型加速复原方法。该方法采用梯度下降法求解图像复原问题，并通过若干小型神经网络进行展开与加速。这些神经网络的作用是在每一步迭代中修正解的估计值。模型经过训练，能够利用精选数据集在固定的小步数内完成优化。研究结果表明，相较于标准优化流程，这两种方法均能显著缩短复原时间。我们还展示了这些模型可通过三个不同仪器的观测图像以无监督方式进行训练。值得注意的是，当应用于新数据集时，它们同样展现出强大的泛化能力。为促进进一步的研究与合作，我们已向科学界公开提供训练好的模型、相应的训练与评估代码以及训练数据集。