Abstract
Due to the sparsity and inhomogeneity of sampling points in spatial frequency domain, the effectiveness of the rules for finding two points in the target object from the autocorrelation is limited, and the reconstructed image is blurred. Traditional segmented planar imaging usually needs to enhance image sharpness and minimize artifacts with continuous modification to the reconstruction algorithm. However, if the ideal image quality is not high, there will be less space for actual image optimization after sampling. To solve this problem, a segmented planar imager based on dense azimuthal sampling lens array is proposed in this paper. The radial fill factor of the lens array is 0.5, and the number of radial-spoke photonic integrated circuits (PIC) is twice that of the traditional system, which can effectively mitigate image artifacts and improve ideal image quality. Based on the dense azimuth sampling lens array architecture, the full-chain theoretical model is established, a discrete spectrum matrix reconstruction method is proposed to reduce the space between spatial sampling points. With this method, it can achieve the continuous sampling of all integer multiples fundamental frequency within the highest frequency range including zero frequency along the baseline direction. In addition, the number of radial-spoke PICs and the effective spatial sampling radius are further simulated. The results show that the upper limit of the peak signal-to-noise ratio (PSNR) can be improved by increasing the number of azimuthal sampling PICs, and reducing the effective spatial sampling radius can weaken the noise and enhance the definition of the actual image. The research results of system performance have certain reference significance for the design of segmented planar imagers in optimizing the number of radial-spoke PICs. The method of combining structural design and sampling is of great significance for improving the imaging quality of the system.
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