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Scalability conjecture for the Fried parameter in synthesis of turbulent atmosphere point spread functions

JOURNAL OF APPLIED REMOTE SENSING 2018年第4期12卷

作者： Hunt, Bobby R. Iler, Amber L. Rucci, Michael A. Integr Applicat Inc Pacific Def Solut Kihei HI 96753 USA Integr Applicat Inc Ann Arbor MI 48108 USA US Air Force Res Lab Wright Patterson AFB OH 45433 USA

A common method for synthesizing turbulent imagery is to model phase perturbations on a wavefront and then propagate the wavefront to the entrance pupil of an imaging system. The point spread function (PSF) that results from the wavefront in the pupil is then computed and used to synthesize images by the usual means of convolution. In a recent publication, a method was disclosed using sparse and redundant dictionaries of turbulent characteristics to construct PSFs directly in the image plane and simulate image formation without making phase models and computing wavefront propagation. However, the dictionary method, as disclosed in the recent publication, is limited to modeling PSFs characterized by the Fried parameter of the data used to construct the dictionary. Herein, we demonstrate that a dictionary constructed from data with a given Fried parameter can be scaled to construct turbulent PSFs corresponding to larger and smaller values of the Fried parameter. This enables a single dictionary, or a small number of dictionaries, to serve for the simulation of turbulent images over a range of turbulence conditions. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： image modeling and simulation turbulence sparse and redundant representations point spread functions atmospheric optics remote sensing image restoration

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Synthesis of atmospheric turbulence point spread functions by sparse and redundant representations

引用

OPTICAL ENGINEERING 2018年第2期57卷 024101-024101页

作者： Hunt, Bobby R. Iler, Amber L. Bailey, Christopher A. Rucci, Michael A. Integr Applicat Inc Pacific Def Solut Kihei HI 96753 USA Integr Applicat Inc Ann Arbor MI 48108 USA Leidos Beavercreek OH USA Air Force Res Lab Wright Patterson AFB OH USA

Atmospheric turbulence is a fundamental problem in imaging through long slant ranges, horizontal-range paths, or uplooking astronomical cases through the atmosphere. An essential characterization of atmospheric turbulence is the point spread function (PSF). Turbulence images can be simulated to study basic questions, such as image quality and image restoration, by synthesizing PSFs of desired properties. In this paper, we report on a method to synthesize PSFs of atmospheric turbulence. The method uses recent developments in sparse and redundant representations. From a training set of measured atmospheric PSFs, we construct a dictionary of "basis functions" that characterize the atmospheric turbulence PSFs. A PSF can be synthesized from this dictionary by a properly weighted combination of dictionary elements. We disclose an algorithm to synthesize PSFs from the dictionary. The algorithm can synthesize PSFs in three orders of magnitude less computing time than conventional wave optics propagation methods. The resulting PSFs are also shown to be statistically representative of the turbulence conditions that were used to construct the dictionary. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： image modeling and simulation turbulence sparse and redundant representations point spread functions remote sensing image restoration

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