Computer vision at low light

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.

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.