Dunhuang-Image-Restoration

final project of NTU DLCV fall2019

Dunhuang Image Restoration using partial convolution and SSIM loss \ by 鄭揚，李信穎，唐瑋廷

Introduction

In this project, we employed NVIDIA’s Partial Convolutions with an U-Net architecture and structural similarity loss function to restore incomplete Dunhuang images. The model surpassed baseline with quantitative evaluation and produced high-quality restoration result.

The original challenge website is provided here.

Result

Dataset

To get the dataset for this project, please use the following command:

bash download.sh

All the data is under ./Data_Challenge2, and the dataset is aranged as below:

• train/ : 400 damaged images ( _masked.jpg) and corresponding masks ( _mask.jpg) for training.
• train_gt/ : 400 ground truth images (* .jpg) for training.
• test/ : 100 damaged images ( _masked.jpg) and corresponding masks ( _mask.jpg) for testing.
• test_gt/ : 100 ground truth images (* .jpg) for testing.

Inference

To install dependencies

pip3 install -r requirements.txt

To execute script

CUDA_VISIBLE_DEVICES=GPU_NUMBER bash final.sh $1 $2

• $1 is the source folder (e.g. Data_Challenge2/test/)of testing images (The contents are the same as Data_Challenge2/test/)
• $2 is the folder (e.g. Data_Challenge2/pred/) to save the restored images (predicted images). If the input image is name xxx.jpg, the outputed image will be name xxx.jpg.

Evaluation

We evaluate the quality of restored images with Mean Square Error (MSE) and Structural Similarity Index (SSIM).

python3 evaluate.py -g $1 -p $2

• $1 : folder of ground truth images. (e.g. ./Data_Challenge2/test_gt/).
• $2 : folder of your predicted images. (e.g. Data_Challenge2/pred/)