A Linear Systems Approach to Imaging Through Turbulence

被引：0
作者：
Mario Micheli
Yifei Lou
Stefano Soatto
Andrea L. Bertozzi
机构：
[1] UCLA,Department of Mathematics
[2] Georgia Inst. of Technology,School of Electrical and Computer Engineering
[3] UCLA,Department of Computer Science
来源：
Journal of Mathematical Imaging and Vision | 2014年 / 48卷
关键词：
Image processing; Atmospheric turbulence; Deblurring; Kalman filter;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper we address the problem of recovering an image from a sequence of distorted versions of it, where the distortion is caused by what is commonly referred to as ground-level turbulence. In mathematical terms, such distortion can be described as the cumulative effect of a blurring kernel and a time-dependent deformation of the image domain. We introduce a statistical dynamic model for the generation of turbulence based on linear dynamical systems (LDS). We expand the model to include the unknown image as part of the unobserved state and apply Kalman filtering to estimate such state. This operation yields a blurry image where the blurring kernel is effectively isoplanatic. Applying blind nonlocal Total Variation (NL-TV) deconvolution yields a sharp final result.
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页码：185 / 201
页数：16
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