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24 November 2009 Image restoration for sparse aperture systems based on wavelet-Wiener algorithm
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The sparse aperture system, which utilizes several sub-apertures in place of monolithic surface, has attracted more interests because it has the advantages of lower cost and lighter weight, and keeps the same aperture size to reach the demanded angular resolution. However the image quality degrades because of smaller effective aperture areas. When the diffraction-limited sparse aperture optical system is imaging ideally, since the optical transfer function is known in the diffraction-limited sparse aperture system, wiener filter is thought to be the best tool to restore the images. In the actual imaging process, an image must been disturbed by varieties of noises so that the ability of Wiener filtering image restoration degrades obviously, the restoration effect of the images with noises by using Wiener filtering is not to be efficient. This paper proposes an improved de-noising algorithm by analyzing traditional wavelet threshold de-noising method. For the images created by using the simulated sparse aperture optical system, first, we can remove the noises in the images using the improved wavelet threshold method and enhance the signal-to-noise ratio, and then obtain the more ideal image formation in the greatest degree, and finally perform restoration of the preprocessed images based on the improved Wiener filtering method. The simulated experiments are fulfilled with sparse aperture system Golay6 with different fill factors designed with the aid of the optical designing software system ZEMAX. The simulated results demonstrate that the algorithm proposed in this paper is superior to normal Wiener filtering or the improved Wavelet-Wiener filtering method.
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Xifang Zhu and Feng Wu "Image restoration for sparse aperture systems based on wavelet-Wiener algorithm", Proc. SPIE 7513, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology, 75131B (24 November 2009);

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