Research On The Image Restoration Algorithms Of The Sparse Aperture Optical Imaging Systems

Sparse aperture optical imaging systems is synthesized by several space distributed and coherent small sub-aperture optical systems. In order to properly combine the beam through the sub-aperture on focus plane of the optical system, the location of each sub-aperture must be equalization phased and be controlled precisely so as to provide the diffraction-limited resolution as that of the corresponding single large aperture system. The sparse aperture systems make the large aperture possible by successfully avoiding the difficulty of processing technology, production cost and limitation of capacity and weight. The paper makes a research on the basic theory of the sparse aperture optical systems and the technology of the image restoration.The importance, significance and reason of why the paper studies on the sparse aperture is indicated at first. The state-of-art of the sparse aperture optical imaging systems is introduced. Then starting from the basic theory of the sparse aperture optical imaging systems, the paper studies on the typical structure, the characteristics, the point spread function(PSF) and the optical transfer function(OTF). The PSF and OTF of the Golay3 aperture is compared with the single aperture.The characteristics of the random signal is introduced as the majority noise is random signal. The random noise is analysed such as the processing and power spectrum of the random noise. A model is built on the gaussian white noise to analyse the random noise.The most important emphasis is on the image restroration. Usually there are two types of the image restoration: constrained restoration and unconstrained restoration. According to different situation, two models of the image restoration for sparse aperture optical systems are proposed. The optimized principle of the image restoration is pointed out, such as the mean square error(MSE). The adapted condition is analysed about Wiener filtering, least squares filtering and maximum likelihood estimation(MLE) blind deconvolution. According to the experiment, the issue points out Wiener filtering and least squares filtering use cmara lens' OTF as the whole system MTF. In theory, the Wiener filtering could make the best image restoration. Considering sparse aperture optical systems with noises, the MLE blind deconvolution uses the atmosphere transfer function and the lens' OTF as the system MTF to have an image restoration. The result is better than Wiener filtering using constant K and least squares filtering.The three algorithms are simulated in MATLAB, using resolution board and aerial photo as the target. The result is compared on the restoration image. The image quality evaluation and error analyse are given. At last, the main research content is summarized, and the future work is prospected.