Sponsor
Portland State University. Department of Computer Science
First Advisor
Atul Ingle
Term of Graduation
Spring 2024
Date of Publication
6-7-2024
Document Type
Thesis
Degree Name
Master of Science (M.S.) in Computer Science
Department
Computer Science
Language
English
Subjects
Computational imaging, Resource-constrained imaging, Single-Photon Cameras, Single-photon imaging, SPAD
Physical Description
1 online resource (x, 64 pages)
Abstract
The modern world is built of images. However, in our goal to photograph and replicate what the human eye is capable of seeing, we are throttled by the restrictions of conventional imaging sensors in high- and low-illumination environments. Single-photon cameras (SPCs) have recently emerged as a promising alternative to conventional camera sensors for capturing images in challenging conditions such as high-dynamic range and fast scene motion. Compared to traditional CMOS cameras, SPCs exploit the arrival of individual photons rather than using an aggregate photon count to compute the brightness of pixels. However, SPCs are extremely resource-intensive, making them inconvenient for power-limited applications such as smartphones, mobile devices for augmented and virtual reality, low-power drones, and autonomous robots. In many imaging settings, we are restricted by the volume of photon data that can be stored and processed by individual pixels in large format (megapixel or larger) SPCs.
The thesis of our work is that SPCs can reconstruct high quality images with far fewer photons per pixel than previously thought necessary. To show this, we present an imaging pipeline including a resource-constrained multiplexing capture algorithm and a state-of-the-art deep learning-based denoiser method using transformers. In particular, we address a fundamental question in single-photon imaging---what is the minimum number of photons needed per pixel to construct an accurate image, and how few photons can one receive before an image is no longer recoverable? Our experiments on a large image dataset show that our pixel-sharing and post-processing denoising techniques can improve image quality by up to 5 dB peak-signal-to-noise ratio in low-light settings when compared to baseline capture methods. By allowing groups of pixels to share in-pixel memory and computing resources, our methods will lower the circuit and hardware complexity of future SPCs without compromising image quality.
Rights
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Persistent Identifier
https://archives.pdx.edu/ds/psu/42322
Recommended Citation
Kurzenhauser, Daphne Ariadne, "Resource-constrained 2D Scene Recovery with Single-Photon Cameras" (2024). Dissertations and Theses. Paper 6656.