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3D deconvolution of adaptive-optics corrected retinal images

机译:自适应光学校正后的视网膜图像的3D反卷积

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摘要

We report on a deconvolution method developed in a Bayesian framework for adaptive-optics corrected images of the human retina. The method takes into account the three-dimensional nature of the imaging process; it incorporates a positivity constraint and a regularization metric in order to avoid uncontrolled noise amplification. This regularization metric is designed to simultaneously smooth noise out and preserve edges, while staying convex in order to keep the solution unique. We demonstrate the effectiveness of the method, and in particular of the edge-preserving regularization, on realistic simulated data.
机译:我们报告了在贝叶斯框架中为人类视网膜的自适应光学校正图像开发​​的反卷积方法。该方法考虑了成像过程的三维性质。它结合了正约束和正则化度量,以避免不受控制的噪声放大。此正则化度量旨在同时消除噪声和保留边缘,同时保持凸面,以保持解决方案的唯一性。我们在实际的模拟数据上证明了该方法的有效性,尤其是边缘保留正则化的有效性。

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