Super-resolution reconstruction in a computational compound-eye imaging system
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From consumer electronics to biomedical applications, device miniaturization has shown to be highly desirable. This often includes reducing the size of some optical systems. However, diffraction effects impose a constraint on image quality when we simply scale down the imaging parameters. Over the past few years, compound-eye imaging system has emerged as a promising architecture in the development of compact visual systems. Because multiple low-resolution (LR) sub-images are captured, post-processing algorithms for the reconstruction of a high-resolution (HR) final image from the LR images play a critical role in affecting the image quality. In this paper, we describe and investigate the performance of a compound-eye system recently reported in the literature. We discuss both the physical construction and the mathematical model of the imaging components, followed by an application of our super-resolution algorithm in reconstructing the image. We then explore several variations of the imaging system, such as the incorporation of a phase mask in extending the depth of field, which are not possible with a traditional camera. Simulations with a versatile virtual camera system that we have built verify the feasibility of these additions, and we also report the tolerance of the compound-eye system to variations in physical parameters, such as optical aberrations, that are inevitable in actual systems.
KeywordsSuper-resolution Compound-eye Phase-mask
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