论文标题
有关计算成像的机器学习方法的注释
A Note on Machine Learning Approach for Computational Imaging
论文作者
论文摘要
计算成像在自然科学的发展中起着至关重要的作用。感官，信息和计算机技术的进步进一步扩大了成像的影响范围，使数字图像成为我们日常生活的重要组成部分。在过去的三十年中，我们目睹了计算成像中数学和机器学习方法的惊人发展。在本说明中，我们将回顾用于计算成像的机器学习方法的最新发展，并讨论其与数学方法的差异和关系。我们将演示如何结合两种方法中的智慧，讨论这种组合的优点和潜力，并提出一些新的计算和理论挑战。
Computational imaging has been playing a vital role in the development of natural sciences. Advances in sensory, information, and computer technologies have further extended the scope of influence of imaging, making digital images an essential component of our daily lives. For the past three decades, we have witnessed phenomenal developments of mathematical and machine learning methods in computational imaging. In this note, we will review some of the recent developments of the machine learning approach for computational imaging and discuss its differences and relations to the mathematical approach. We will demonstrate how we may combine the wisdom from both approaches, discuss the merits and potentials of such a combination and present some of the new computational and theoretical challenges it brings about.
