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Atmospheric Turbulence Degraded Image Restoration using Principal Components Analysis

Li, Dalong; Mersereau, Russell M.; Simske, Steven

External - Copyright Consideration

Keyword(s): Principal Components Analysis; blind image deconvolution; Lucy-Richardson algorithm; atmospheric turbulence

Abstract: Our earlier work revealed a connection between blind image deconvolution and Principal Components Analysis (PCA). In this letter, we explicitly formulate multichannel and single-channel blind image deconvolution as a PCA problem. Although PCA is derived from blur models that do not contain additive noise, it can be justified both on theoretical and experimental grounds that the PCA-based restoration algorithm is actually robust to the presence of white noise. The algorithm is applied to the restoration of atmospheric turbulence degraded imagery and compared to an adaptive Lucy-Richardson maximum likelihood (LR) algorithm on both real and simulated atmospheric turbulence blurred images. It is shown that the PCA- based blind image deconvolution runs faster and is more robust to noise. Publication Info: To be published in IEEE Geoscience and Remote Sensing Letters, 2007

5 Pages

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