基于空间变卷积神经仿真器的太阳多目标多帧盲反卷积
Solar multi-object multi-frame blind deconvolution with a spatially variant convolution neural emulator
AI总结：
针对地基太阳观测受大气湍流畸变影响、传统反卷积方法效率低易生伪影的问题，提出基于深度神经网络的空间变卷积仿真器框架，用作多目标多帧盲反卷积前向模型，无需斑块拼接，大幅提升反卷积效率与准确性。
摘要：
通过地基观测研究天文现象始终受到地球大气畸变效应的挑战。作为校正这些畸变的必要手段，传统事后图像校正方法通常依赖简化假设，这限制了其有效性，尤其是在存在空间变化大气湍流的情况下。这类问题通常通过将视场划分为小斑块、独立反卷积每个斑块再合并所有斑块的方式解决，该方法往往效率低下且可能产生伪影。计算技术的最新进展与深度学习的出现为解决这些局限提供了新途径。本文提出一种利用深度神经网络仿真空间变卷积的新型框架，在天文图像反卷积的效率和准确性上实现了突破。通过在经空间不变点扩散函数卷积的图像数据集上训练，并验证其对空间变化条件的泛化能力，该方法相比传统方法取得了显著进步。该卷积仿真器在太阳图像多目标多帧盲反卷积算法中用作前向模型，可对大视场太阳观测数据进行反卷积，无需采用逐斑块拼接方式，从而避免了这类技术带来的伪影。该方法具备显著的计算优势，可将处理时间缩短数个数量级。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
