Foreword to special issue of Inverse Problems on modern challenges in imaging
Foreword to special issue of Inverse Problems on modern challenges in imaging
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关于现代成像挑战的反问题特刊前言
DOI：
10.1088/1361-6420/acb569
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发表时间：
2023
期刊：
影响因子：
2.1
通讯作者：
Rigaud, Gaël
中科院分区：
文献类型：
--
作者：
Hahn, Bernadette N;Quinto, Eric Todd;Rigaud, Gaël
这期《逆问题》特刊是为了纪念诺贝尔奖获得者Allan Cormack（1924-1998）的成就，他的开创性工作为计算机断层扫描提供了数学基础。它收集了关于成像逆问题的顶级文章，包括广泛的断层成像方式，数学和应用。反问题，通常是从间接数据重建图像，现在在人类生活的许多方面发挥着至关重要的作用，如医疗保健，国家安全，无损检测和遥感。成像技术的不断进步带来了无数的可能性，推动研究人员克服新的理论和实践挑战，并对日常生活产生了巨大的影响。本期特刊共收录高质量原创研究论文29篇。他们代表了广泛的逆问题，从理论和计算的角度来看，都与现代成像的挑战。大多数物理过程中涉及的逆问题是不适定的，需要适当的正则化方案来稳定逆和重建过程。许多新应用的出现以及诸如成本、时间、访问等约束的演变，以及因此对更高效、更小和更便宜的系统的需求，导致正则化和重建方案方面的新挑战。本期特刊为这些挑战提供了许多答案。[19]提供了对1991年Tikhonov正则化的深刻而新鲜的看法。[13]采用ADMM方法进行全变差近视去卷积。[3]提出了基于分数阶拉普拉斯算子的双层优化神经网络求解反问题。[17]提出了一种通过构造自适应平滑算子来改进Tikhonov正则化的内-外方法。[11]研究了带约束的极小化问题，并提出了相应的基于模的迭代格式。在[23]中，作者发展了贝叶斯理论，
This special issue of Inverse Problems honours the achievements of Nobel Laureate Allan Cormack (1924–1998) whose pioneering work provided mathematical foundations of computerized tomography. It gathers top articles on inverse problems in imaging, including a broad range of tomographic modalities, mathematics, and applications. Inverse problems, typically image reconstruction from indirect data, now play a crucial role in many aspects of human life such as healthcare, national security, non-destructive testing, and remote sensing. The relentless progress in imaging technology opens myriad possibilities, pushes researchers to overcome new theoretical and practical challenges, and has an enormous impact on everyday life. Twenty-nine high-quality original research papers have been collected in this special issue. They represent a broad range of inverse problems, from theoretical and computational perspectives, and are all related to modern challenges in imaging. Most inverse problems involved in physical processes are ill-posed, requiring suitable regularization schemes to stabilize the inverse and the reconstruction process. The emergence of numerous novel applications as well as the evolution of constraints such as costs, time, access, etc and hence the need for more efficient, smaller, and cheaper systems, lead to new challenges in terms of regularization and reconstruction schemes. This special issue offers many answers to these challenges.[19] provides a deep and fresh look at ℓ1 Tikhonov regularization.[13] adapts the ADMM method for total-variation myopic deconvolution.[3] proposes bilevel optimization neural networks based on the fractional Laplacian to solve inverse problems.[17] also addresses the question of edge-preserving regularization method and works out an inner-outer approach that improves Tikhonov regularization by constructing adaptive smoothing operators.[11] studies ℓp-ℓq constrained minimization problems and develops associated modulus-based iterative schemes. In [23], the authors develop the theory of Bayesian
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