Low-light Image Analysis

Low-light imaging is often needed for various purposes, such as surveillance, photography and autonomous driving. In particular for autonomous driving, day-time and night-time each roughly contributes to 50% of the time over a year, and it is equally important for computer vision techniques developed for day-time scenes to work at night-time scenes. Unfortunately, low-light images typically contain very dark regions, which may suffer from under-exposure problems (i.e., their values are very close to zero), while night-time images may suffer from both under-exposure as well as over-exposure problems (i.e., their values may be very close to either zero or one). Enhancing these images or processing them with existing computer vision algorithms often do not work.

In this project, we are developing techniques to process low-light images. Our research is to address this problem from two directions. The first is to consider how to enhance these images to improve their visibility. The second is to investigate how to improve existing computer vision algorithms for direct analyses of low-light images.

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