We present a lensless snapshot hyperspectral camera that is capable of hyperspectral imaging over a broad spectrum using a compact and low-cost hardware configuration. We leverage the multiplexing capability of a lensless camera, a novel type of computational imaging device that replaces the lens with a thin mask. Our device utilizes a linear variable filter and a phase mask to encode spectral information onto a monochromatic image sensor, enabling recovery of hyperspectral image stacks from a single measurement by utilizing spectral information encoded in different parts of the 2D point spread function. We perform spectral calibration using a reference color chart and verify the prototype device’s spectral and spatial resolution, as well as its imaging field of view. We report on the design and construction of the device, the image reconstruction algorithm, and spectral calibration methods and present hyperspectral images ranging from 410 to 800 nm obtained with our prototype device.
Computational microscopy, which merges cutting-edge optical methods with intricate algorithms, offers significant potential for applications such as resolution improvement and quantitative phase retrieval. However, it faces challenges due to high computational demands and the need for precise algorithms. Recent advancements in data-driven deep-learning-based techniques have emerged to mitigate these challenges; however, incorporating physics-based constraints can further address the limitations. In this paper, we propose a deep-learning framework for image reconstruction in computational microscopy that combines the advantages of single-pass, end-to-end inversion and physics-informed learning approaches, while considering the challenges in obtaining exact physics-informed constraints. Our network learns the forward imaging process, which accurately estimates position-dependent optical aberrations and serves as an effective physical prior in the training of the reconstruction model. Validated on Fourier Ptychography (FP), our proposed framework demonstrates fast and robust FP reconstructions that outperform conventional model-based methods with significantly fewer input measurements and exhibits generalizability to unseen samples, as demonstrated by quantitative comparisons with existing methods. We evaluated our approach on various datasets, including human breast cancer pathology samples, the HeLA dataset, and the USAF phase target dataset, demonstrating that our method improves the practicality of computational microscopy by offering high-quality reconstructions with faster speeds over different sample types.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.