Optical synthetic aperture system imaging enhancement method based on sparse prior

Abstract

The invention discloses an optical synthetic aperture system imaging enhancement method based on sparse prior. The method is used for solving the problem of imaging degradation of an optical synthetic aperture system caused by the influence of an array structure and co-phase errors. The method is characterized in that a dark channel and image gradient prior are used as sparse prior to design an imaging enhancement model, and the method comprises the following steps: firstly, calculating a point spread function under a synthetic aperture system array structure as initial blur kernel input, and then solving the sparse prior through a semi-quadratic splitting method; the solution model estimates a final blur kernel and images enhanced. By utilizing the inherent prior theory of the synthetic aperture system, the method has the advantages of wide application range, simplicity in implementation, good enhancement effect and the like.

Description

technical field [0001] The invention relates to the technical fields of image processing and optical engineering, in particular to the field of image restoration and enhancement of optical synthetic aperture systems, and in particular to a sparse prior-based imaging enhancement method of optical synthetic aperture systems. Background technique [0002] Optical synthetic aperture systems use smaller, easier-to-manufacture apertures arranged in space to achieve resolution equivalent to a single large aperture. The serious problem it faces is the degradation and blurring of the image, which is mainly caused by the reduction of the light transmission area caused by the array structure and the PSF dispersion caused by the common phase error. Therefore, it needs to be solved from the perspective of imaging enhancement. [0003] At present, the commonly used imaging enhancement method for synthetic aperture system is the Wiener filter method. Wiener filtering is based on the prin...

AI Technical Summary

Problems solved by technology

Wiener filtering has a good restoration effect on known PSF and noise types, but when the noise power spectrum cannot be better estimated, the restoration effect is poor, and at the same time it cannot solve the phase error, and the restored image will produce ringing.

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