用深度学习提升湍流中成像清晰度，助力安防与航天应用
Computational Imaging Through Atmospheric Turbulence
- 结合统计光学与深度学习构建湍流成像新模型
- 显著改善复杂大气条件下的图像重建质量
- 适合图像处理与计算机视觉方向的研究者参考
自20世纪40年代安德烈·柯尔莫戈罗夫的开创性工作以来，大气湍流中的成像已从纯科学探索发展为众多民用、航天任务及国家安全应用的重要课题。得益于深度学习的最新进展，该领域正迎来新一轮发展浪潮。然而，为使深度学习方法表现优异，亟需构建更快、更准确的计算模型，同时最大化图像重建性能。本书主要面向图像处理工程师、计算机视觉科学家及工程专业学生，涵盖大气湍流、统计光学与图像处理等内容，可作为研究生教材或本科生高阶课程材料。
原文摘要 · Abstract (English)
Since the seminal work of Andrey Kolmogorov in the early 1940's, imaging through atmospheric turbulence has grown from a pure scientific pursuit to an important subject across a multitude of civilian, space-mission, and national security applications. Fueled by the recent advancement of deep learning, the field is further experiencing a new wave of momentum. However, for these deep learning methods to perform well, new efforts are needed to build faster and more accurate computational models while at the same time maximizing the performance of image reconstruction. The book is written primarily for image processing engineers, computer vision scientists, and engineering students who are interested in the field of atmospheric turbulence, statistical optics, and image processing. The book can be used as a graduate text, or advanced topic classes for undergraduates.
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