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October 2012

Volume 51, Issue 10 (partial)

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Technique for simulating anisoplanatic image formation over long horizontal paths

Jeremy P. Bos and Michael C. Roggemann

Opt. Eng. 51, 101704 (May 15, 2012); http://dx.doi.org/10.1117/1.OE.51.10.101704

Online Publication Date: May 15, 2012

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The presence of turbulence over horizontal imaging paths severely reduces the resolution available to imaging systems and introduces anisoplanatic distortions in the image frame. A variety of image processing techniques are being developed which can mitigate these effects, and sequences of high-fidelity images created via simulation are useful in their development. In this paper, we describe a simplification of the split-step wave propagation method that employs a series of uniformly spaced phase screens to accurately simulate turbulence effects on imaging over horizontal paths. Employing this method, a series of 1000 image frames were generated for each of three turbulence conditions. The mean squared error in intensity per pixel is also evaluated for each frame in comparison to a diffraction-limited reference. Examination of the per-frame intensity error statistics indicate these errors are log-normally distributed about a mean value that increases approximately linearly with turbulence strength.

Multi-resolution remote sensing image registration using differential evolution with adaptive strategy selection

Zhenbang Hu, Wenyin Gong, and Zhihua Cai

Opt. Eng. 51, 101707 (May 15, 2012); http://dx.doi.org/10.1117/1.OE.51.10.101707

Online Publication Date: May 15, 2012

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An algorithm based on differential evolution (DE) with adaptive strategy selection for remote sensing image registration is designed. The paper has two major contributions. One contribution is the reduction of the computation time for a better result, i.e., fitness function approximate, which is based on multi-resolution processing and histogram matching. The second contribution is an adaptive strategy known as probability matching integration. The probability matching technique is employed in DE to autonomously select the most suitable strategy while solving the problem. To the best of our knowledge, the proposed method is the first adaptive strategy selection-based approach for image registration. In our experiments, the proposed method significantly outperforms previously proposed image registration method based on DE.
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