Complementary coded aperture set for compressive high-resolution imaging

The traditional imaging approach with circular aperture lenses lose the high frequency part of the scene because of limited cut-off frequency of the aperture, which could not be recovered only with the post-processing method. Our analysis of the frequency shows that different apertures have different frequency retention, and a single aperture can not preserve more high frequency information, which brings on unsuccessful reconstruction of High-Resolution(HR) images. So a HR imaging method based on frequency-complementary compressive coded aperture set is proposed. We derive a criterion for evaluating compressive coded aperture set with respect to the spectral complementarity maximization and the precision of restoration. This criterion is optimized with a genetic algorithm to get a set of coded aperture which can preserve more high frequency part together when compressive coded sampling. This property can obtain more details that are missed in each individual coded sensing image but preserved mutually among all coded sensing images. In the reconstruction stage, we utilize the non-local similarity sparse prior and frequency complementarity among multi-images to build a reconstruction model for the HR image recovery. Finally, by combining compressive coded sampling with sensing reconstruction, we construct a coded sampling-reconstruction camera system. Extensive experiments with the sampling-reconstruction system on the simulations as well as the real-world images demonstrate that the proposed method can recover more details and get better results compared with other coded apertures and super-resolution methods.