1. Dynamically Unfolding Recurrent Restorer (DURR)
We propose a new control framework to restore images corrupted by different degradation levels.
The proposed control problem contains a restoration dynamics modeled by an RNN, and the terminal time
is determined by a policy network. The model is called DURR. Experiments show that DURR achieves
state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR
is able to generalize to damage levels not included in the training stage.
2. JPEG Image Deblocking: DMCNN
An end-to-end CNN with large receptive fields exploiting dual-domain multi-scale features. The proposed DMCNN can handle multiple kinds of JPEG compression artifacts, especially banding effects effectively. Experiments show that
DMCNN sets a new state-of-the-art for the task of JPEG artifact removal.
3. HEVC Post Filter
We are trying to develop a convolutional neural network to work as an HEVC post-process filter to remove blocking artifacts and ringing artifacts, especially at low bit-rates.
2. Facial Master
A hackathon project like Apple's animoji. We use a webcam to extract face landmarks and analyze people's expression. Then map these facial expressions to a series of pre-defined open-source face models in the real-time.
3. Chrys: An AI for Multi-Player Pacman
We develop an AI for the game of Pacman on the Botzone (An online AI judge platform) utilizing mainly the Monte Carlo Tree Search (MCTS) and other evaluation techniques. Chrys ranks 1st on
the platform till now. You can view a sample game here. (My Botzone IDs are @BuriedJet and @蒓凊尐囗�?)
1. Ajax: An AI for Reversi
We develop an AI for the game of Reversi on the Botzone (An online AI judge platform). Ajax is based on decision tree searching with alpha-beta pruning and other search techniques (eg. Iterative Deepening and PVS). Featuring a sophisticated evaluation function, Ajax ranked 1st at its birth and maintains the top ten till now. The codes can be found here. You
can play with Ajax online here.