Multi-aperture imaging can improve spatial resolution by fusing complementary information from multiple low-resolution (LR) frames using super-resolution (SR) algorithms. The existing SR algorithms are offline used due to the lack of fast regularization parameter update solution. The classical general cross validation (GCV) and the L -curve algorithms search for a suitable solution on each updated logarithmic parameter set for a long time. In this work, we proposed an adaptive regularization parameter tuning (ARPT) method. The regularization parameter is updated by a function of the fidelity term, the standard deviation of the noise, the weighted bilateral total variation and its standard deviation. Simulations demonstrate that the PSNR and SSIM of the ARPT algorithm, are 0.27 dB and 0.002 larger than the GCV algorithm on average, and the computation time is reduced by more than four times. A four-aperture SR camera is built by randomly shifting the sensors on a sub-pixel scale. An SR image was obtained by the ARPT method using 20 LR images. The average consuming time is reduced by 6.17 times compared with the GCV algorithm. Experiments indicate that the proposed four-aperture SR camera can improve the spatial resolution, suppress the noise, and realize a high-quality and more efficient reconstruction.
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