EDAR

PyTorch implementation of Deep Convolution Network based on EDSR for Compression Artifacts Reduction

Requirements

• PyTorch
• tqdm
• Pillow

Network Architecture

Visual Results

Training

Dataset: DIV 2K train set + ...(custom dataset...)

Batch size: 16

Patch size: 48x48

Optimizer: Adam

Loss: L1 Loss

Input: Compressed Image by JPEG (jpeg_quality: rand(0 to 10)) / RGB

Output: Original Image / RGB

Epoch: 450

Pre-trained weight

How to train

python train.py --images_dir [Your training image path] --outputs_dir ./ --jpeg_quality [10 to 100] --batch_size [num] --num_epochs [num]

Pre-trained model was trained using the below arguments.

python train.py --images_dir ../DIV2K_train_HR --outputs_dir ./ --jpeg_quality 10 --batch_size 16 --num_epochs 200

How to test

python test.py --weights_path [your trained weight].pth --image_path [your_image] --outputs_dir ./