<p>Imaging in low light is difficult because the number of photons arriving at the image sensor is low. This is a major technological challenge for applications such as surveillance and autonomous driving. Conventional CMOS image sensors (CIS) circumvent this issue by using techniques such as burst photography. However, this process is slow and it does not solve the underlying problem that the CIS cannot efficiently capture the signals arriving at the sensors. This dissertation focuses on solving this problem using a combination of better image sensors (Quanta Image Sensors) and computational imaging techniques.</p> <p><br></p> <p>The first part of the thesis involves understanding how the quanta image sensors work and how they can be used to solve the low light imaging problem. The second part is about the algorithms that can deal with images obtained in low light. The contributions in this part include – 1. Understanding and proposing solutions for the Poisson noise model, 2. Proposing a new machine learning scheme called student-teacher learning for helping neural networks deal with noise, and 3. Developing solutions that work not only for low light but also for a wide range of signal and noise levels. Using the ideas, we can solve a variety of applications in low light, such as color imaging, dynamic scene reconstruction, deblurring, and object detection.</p>

<p>Imaging and computer vision systems deployed in real-world environments face the challenge of accommodating a wide range of lighting conditions. However, the cost, the demand for high resolution, and the miniaturization of imaging devices impose physical constraints on sensor design, limiting both the dynamic range and effective aperture size of each pixel. Consequently, conventional CMOS sensors fail to deliver satisfactory capture in high dynamic range scenes or under photon-limited conditions, thereby impacting the performance of downstream vision tasks. In this thesis, we address two key problems: 1) exploring the utilization of spatial multiplexing, specifically spatially varying exposure tiling, to extend sensor dynamic range and optimize scene capture, and 2) developing techniques to enhance the robustness of object detection systems under photon-limited conditions.</p> <p><br></p> <p>In addition to challenges imposed by natural environments, real-world vision systems are susceptible to adversarial attacks in the form of artificially added digital content. Therefore, this thesis presents a comprehensive pipeline for constructing a robust and scalable system to counter such attacks.</p>