About the Project
In this PhD project, we plan to develop a pipeline for enhancing nearly black images/videos taken in low light using Deep Learning and Artificial Intelligence (AI). We will start by developing a supervised learning approach which does require some ground truth pairs of dark images and well-lit images for training. In the next stage, we will further investigate an unsupervised learning framework which does not require any corresponding ground truth image pairs. Instead, the training will be based on analysing different un-annotated sets of dark and well-lit images. We will also compare our solution with start-of-the-art methods (e.g. [I,ii]) using both quantitative evaluation and user experiments.
The outcome of this project is expected to provide powerful applications in smartphone camera development and night-time surveillance.
A start date prior to October 2020 may be possible but this should be discussed with Dr Gong in the first instance.
Project supervisor: https://people.uea.ac.uk/h_gong
Mode of study: Full time
Start date: October 2020
Entry requirements: First degree (2:1 or above) in Computer Science, Mathematics, Physics, Engineering (or other related STEM subjects).
A bench fee is also payable on top of the tuition fee to cover specialist equipment or laboratory costs required for the research. Applicants should contact the primary supervisor for further information about the fee associated with the project.
Guo, Xiaojie, Yu Li, and Haibin Ling. "LIME: Low-light image enhancement via illumination map estimation." IEEE Transactions on Image Processing 26.2 (2017): 982-993.
Hu, Zhe, et al. "Deblurring Low-light Images with Light Streaks." IEEE Transactions on Pattern Analysis and Machine Intelligence (2017).
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