Browsing by Author "Tan, Jasper"

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    EDoF-ToF: extended depth of field time-of-flight imaging
    (Optical Society of America, 2021) Tan, Jasper; Boominathan, Vivek; Baraniuk, Richard; Veeraraghavan, Ashok
    Conventional continuous-wave amplitude-modulated time-of-flight (CWAM ToF) cameras suffer from a fundamental trade-off between light throughput and depth of field (DoF): a larger lens aperture allows more light collection but suffers from significantly lower DoF. However, both high light throughput, which increases signal-to-noise ratio, and a wide DoF, which enlarges the system’s applicable depth range, are valuable for CWAM ToF applications. In this work, we propose EDoF-ToF, an algorithmic method to extend the DoF of large-aperture CWAM ToF cameras by using a neural network to deblur objects outside of the lens’s narrow focal region and thus produce an all-in-focus measurement. A key component of our work is the proposed large-aperture ToF training data simulator, which models the depth-dependent blurs and partial occlusions caused by such apertures. Contrary to conventional image deblurring where the blur model is typically linear, ToF depth maps are nonlinear functions of scene intensities, resulting in a nonlinear blur model that we also derive for our simulator. Unlike extended DoF for conventional photography where depth information needs to be encoded (or made depth-invariant) using additional hardware (phase masks, focal sweeping, etc.), ToF sensor measurements naturally encode depth information, allowing a completely software solution to extended DoF. We experimentally demonstrate EDoF-ToF increasing the DoF of a conventional ToF system by 3.6 ×, effectively achieving the DoF of a smaller lens aperture that allows 22.1 × less light. Ultimately, EDoF-ToF enables CWAM ToF cameras to enjoy the benefits of both high light throughput and a wide DoF.
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    Face detection and verification with FlatCam lensless imaging system
    (2018-08-09) Tan, Jasper; Baraniuk, Richard G
    Progress in any technological area requires distinct breakthrough ideas. In the field of imaging, lensless imaging technology is a disruptive concept that allows cameras to continue getting thinner and cheaper. The FlatCam lensless imaging system demonstrates this by replacing the thick and expensive lens of a conventional camera with a thin and cheap aperture mask and a reconstruction algorithm. Indeed, such a design allows recognizable image capture, albeit with much lower resolution and greater noise than conventional lens-based cameras. The true disruptive ability of FlatCam in society is its potential to fuel a machine's capability of obtaining a wealth of information from the world via images, a common step in the pipeline of machine intelligence. In this work, I rigorously demonstrate and evaluate performing face detection and verification, two such intelligence tasks, with FlatCam images. To perform face detection and verification, I propose and adapt basic deep learning techniques to handle the resolution, noise, and artifacts inherent with the FlatCam. I show with common evaluation protocols that there is only a small decrease in accuracy when using FlatCam images compared to the original lens-based images. Furthermore, I describe the construction of a face dataset captured with a FlatCam prototype containing 23,368 lensless camera images of 92 subjects in a range of different operating conditions. Further evaluating face verification on this dataset verifies the FlatCam's potential for performing inference tasks in real-world deployment.
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    Privacy-Preserving Machine Learning: The Role of Overparameterization and Solutions in Computational Imaging
    (2022-12-02) Tan, Jasper; Baraniuk, Richard G.
    While the accelerating deployment of machine learning (ML) models brings benefits to various aspects of human life, it also opens the door to serious privacy risks. In particular, it is sometimes possible to reverse engineer a given model to extract information about the data on which it was trained. Such leakage is especially dangerous if the model's training data contains sensitive information, such as medical records, personal media, or consumer behavior. This thesis is concerned with two big questions around this privacy issue: (1) "what makes ML models vulnerable to privacy attacks?" and (2) "how do we preserve privacy in ML applications?". For question (1), I present detailed analysis on the effect increased overparameterization has on a model's vulnerability to the membership inference (MI) privacy attack, the task of identifying whether a given point is included in the model's training dataset or not. I theoretically and empirically show multiple settings wherein increased overparameterization leads to increased vulnerability to MI even while improving generalization performance. However, I then show that incorporating proper regularization while increasing overparameterization can eliminate this effect and can actually increase privacy while preserving generalization performance, yielding a ``blessing of dimensionality'' for privacy through regularization. For question (2), I present results on the privacy-preserving techniques of synthetic training data simulation and privacy-preserving sensing, both in the domain of computational imaging. I first present a training data simulator for accurate ML-based depth of field (DoF) extension for time-of-flight (ToF) imagers, resulting in a 3.6x increase in a conventional ToF camera's DoF when used with a deblurring neural network. This simulator allows ML to be used without the need for potentially private real training data. Second, I propose a design for a sensor whose measurements obfuscate person identities while still allowing person detection to be performed. Ultimately, it is my hope that these findings and results take the community one step closer towards the responsible deployment of ML models without putting sensitive user data at risk.