Abstract

Computational imaging based on projected patterns is an alternative technique to conventional imaging and removes the need for arrayed detectors. Over the last decade, compressive sensing (CS) has emerged as a potential way to perform computational imaging and has found applications in various fields. One approach with which to realize CS is the single-pixel camera (SPC), which makes it possible to acquire an object scene using a small number of coded measurements with a low-cost single-pixel sensor. A computational reconstruction algorithm is then used to recover the image from the linear measurements, which are far fewer than the number of pixels being reconstructed. Unfortunately, the noise suppression of an SPC imposes a challenging issue for reconstructing high-quality images. Nonetheless, realizing multispectral imaging with an SPC is attractive. In this paper, we present a fiber signal collection SPC that achieves high-signal-to-noise visible light imaging and multispectral imaging. We use a fiber to collect and transport the light through an ideal transmission path to replace the lens collection scheme. We develop a proof-of-concept prototype using fiber collection and analyze the system performance on object reflectivity, active area of the detector, and system noises. The fiber-based prototype system provides noticeable improvements in image quality (correlation coefficient) and the ability of multispectral (RGB) imaging compared with a conventional SPC. Our simplified approach to multispectral imaging can be readily extended to other wavebands.

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