Two-Dimensional Superresolving Image And Spectral Restoration Using Linear Programming

George Eichmann; Jaroslav Keybl

doi:10.1117/12.936142

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15 April 1983 Two-Dimensional Superresolving Image And Spectral Restoration Using Linear Programming

George Eichmann, Jaroslav Keybl

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Proceedings Volume 0422, 10th Intl Optical Computing Conf; (1983) https://doi.org/10.1117/12.936142
Event: 10th International Optical Computing Conference, 1983, Cambridge, United States

Abstract

The finite aperture of any physical imaging system eliminates the high spatial-frequency components of the object from appearing in the image. The lack of high frequency detail results in a loss of resolution in the observed image. It has been shown that, for an object of finite extent, an exact restoration of the object from a DL image is possible. However, numerical implementation of the DL image restoration process is highly unstable in the presence of measurement noise. In the dual of the image restoration problem, the extrapolation of a finite segment of the DL (i.e. spatially limited) image data in the presence of measurement noise is performed. It has been found that the imposition of a priori constraints, such as a non-negativity of the estimate, will stabilize the restoration process. In this paper, we employ optimal data fitting techniques that uses linear programming (LP) for optimization. Results of numerical experiments are presented to illustrate the efficacy of this approach.

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George Eichmann and Jaroslav Keybl "Two-Dimensional Superresolving Image And Spectral Restoration Using Linear Programming", Proc. SPIE 0422, 10th Intl Optical Computing Conf, (15 April 1983); https://doi.org/10.1117/12.936142

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