Term: Fall 2018
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Team Group 8
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Team members
- Benedict, Gabriel gb2661@columbia.edu
- Qiaqia, Sun qs2184@columbia.edu
- Zhangyang, Xu zx2229@columbia.edu
- Huiyu, Zhang hz2497@columbia.edu
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Project summary: In this project, we created a classification engine for enhance the resolution of images. We have achieved three things in this project:
- Implemented the current practice as the baseline model.
- Implemented an improvement to the current practice. In this part, we chose two different models as improvements: Xgboost and Neural Network.
- Evaluated the performance gain of our proposed improvement against the baseline.
- Specifically, in feature selection part, we implemented Laplace transformation to training pictures, in order to find the most representaive points in each picture and sampled them as our training data points. Then after building models using training data, through cross validation for each model (mentioned above), we got the best parameters for each model according to the cross validation error, then implemented each model with their best parameters on testing pictures. Finally, according to the PSNR value of each model, we decided which model is the best model in super resolution problem.
Contribution statement: (default) All team members contributed equally in all stages of this project. All team members approve our work presented in this GitHub repository including this contributions statement.
- For the specific assignments:
- Benedict, Gabriel: Finished the feature selection part and baseline model's implementation.
- Qiaqia, Sun: Finished the Neural Network model's implementation and presented group's result.
- Zhangyang, Xu: Finished the Xgboost model's implementation and the readme file.
- Huiyu, Zhang: Finished the super resolution part and baseline model's implementation.
- All memebers took part in group's discussion and brain storming. All members prepared the slides of presentation. All team members contributed to the GitHub repository and prepared the presentation. All team members approve our work presented in our GitHub repository including this contribution statement.
Following suggestions by RICH FITZJOHN (@richfitz). This folder is orgarnized as follows.
proj/
├── lib/
├── data/
├── doc/
├── figs/
└── output/
Please see each subfolder for a README file.