The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post-facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field-of-view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multi-object multi-frame blind deconvolution algorithm for solar images. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude.

该论文介绍了一种利用深度神经网络模拟空间变异卷积的新框架，为天文图像去模糊的效率和准确性带来突破；通过对具有空间不变点扩散函数的图像数据集进行训练，并验证了其适用于空间变异条件，该方法在传统方法上具有显著突破，将卷积模拟器用作太阳图像的多目标多帧盲去模糊算法的前向模型，避免了分块整合技术所带来的伪影现象，从而在处理时间上具有重大的计算优势。

太阳多目标多帧盲反卷积及具有空间变化卷积神经模拟器