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LenslessPiCam: A Hardware and Software Platform for Lensless Computational Imaging with a Raspberry Pi

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arxiv 2206.01430 v1 pith:LT2SU7DU submitted 2022-06-03 eess.IV cs.CV

classification eess.IVcs.CV
keywords imaginglenslesscomputationalhardwareimageslenslesspicamcameracameras
verification ladder T0 review T1 audit T2 compute T3 formal
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Lensless imaging seeks to replace/remove the lens in a conventional imaging system. The earliest cameras were in fact lensless, relying on long exposure times to form images on the other end of a small aperture in a darkened room/container (camera obscura). The introduction of a lens allowed for more light throughput and therefore shorter exposure times, while retaining sharp focus. The incorporation of digital sensors readily enabled the use of computational imaging techniques to post-process and enhance raw images (e.g. via deblurring, inpainting, denoising, sharpening). Recently, imaging scientists have started leveraging computational imaging as an integral part of lensless imaging systems, allowing them to form viewable images from the highly multiplexed raw measurements of lensless cameras (see [5] and references therein for a comprehensive treatment of lensless imaging). This represents a real paradigm shift in camera system design as there is more flexibility to cater the hardware to the application at hand (e.g. lightweight or flat designs). This increased flexibility comes however at the price of a more demanding post-processing of the raw digital recordings and a tighter integration of sensing and computation, often difficult to achieve in practice due to inefficient interactions between the various communities of scientists involved. With LenslessPiCam, we provide an easily accessible hardware and software framework to enable researchers, hobbyists, and students to implement and explore practical and computational aspects of lensless imaging. We also provide detailed guides and exercises so that LenslessPiCam can be used as an educational resource, and point to results from our graduate-level signal processing course.

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Cited by 1 Pith paper

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  1. A generative approach for lensless imaging in low-light conditions

    eess.IV 2025-01 conditional novelty 5.0 of 10

    A new two-stage pipeline for low-light lensless imaging that uses Wiener filtering for a first estimate and a wavelet-domain conditional diffusion model for denoising and brightness enhancement.

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