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Image Restoration - DIV2K

NOTE This repo is based on this Single Image Super Resolution Repo code

Introduction

This repo trains models to do image restoration on DIV2K dataset. It supports all modes of distortion in DIV2K dataset including bicubic, unknown, mild, and difficult. It also restores different downscaling factors 2, 3, 4, and 8. The parameters of the training can be specified so that the model trains on a dataset, and is evaluated on a different one.

Environment setup

create a virtual environment

python3 -m venv .venv

Activate the virtual environment

source .venv/bin/activate

Install the dependencies

pip install -r requirements.txt

DIV2K dataset

For training and validation on DIV2K images, applications should use the provided DIV2K data loader. It automatically downloads DIV2K images to .div2k directory and converts them to a different format for faster loading.

A DIV2K data provider automatically downloads DIV2K training and validation images of given scale (2, 3, 4 or 8) and downgrade operator ("bicubic", "unknown", "mild" or "difficult").

Download Training dataset

from data import DIV2K

train_loader = DIV2K(scale=4,             # 2, 3, 4 or 8
                     downgrade='bicubic', # 'bicubic', 'unknown', 'mild' or 'difficult' 
                     subset='train')      # Training dataset are images 001 - 800
                     
# Create a tf.data.Dataset          
train_ds = train_loader.dataset(batch_size=16,         # batch size as described in the EDSR and WDSR papers
                                random_transform=True, # random crop, flip, rotate as described in the EDSR paper
                                repeat_count=None)     # repeat iterating over training images indefinitely

Download Validation dataset

from data import DIV2K

valid_loader = DIV2K(scale=4,             # 2, 3, 4 or 8
                     downgrade='bicubic', # 'bicubic', 'unknown', 'mild' or 'difficult' 
                     subset='valid')      # Validation dataset are images 801 - 900
                     
# Create a tf.data.Dataset          
valid_ds = valid_loader.dataset(batch_size=1,           # use batch size of 1 as DIV2K images have different size
                                random_transform=False, # use DIV2K images in original size 
                                repeat_count=1)         # 1 epoch

Train Image Restoration models

The repo supports two model architectures.

  • Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR)
  • Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR)

To run training use:

python train_image_resolution_model.py --scale 2
                                       --downgrade bicubic 
                                       --model edsr
                                       --batch-size 16
                                       --depth 16
                                       --scale_val 4
                                       --downgrade_val mild
  • model: Model architecture, can be edsr or wdsr. For quantized models, qedsr can be used.
  • downgrade: Distortion type for training, can be bicubic, unknown, mild, or difficult
  • scale: Downsampling factor for training, can be 2, 3, 4, or 8
  • depth: Depth of the model, default 16
  • batch-size: Training batch size, default 16
  • downgrade_val: Distortion type for validation, can be bicubic, unknown, mild, or difficult
  • scale_val: Downsampling factor for validation, can be 2, 3, 4,8
  • pretrained: Path to the pretrained model.
  • eval_all_distortions: Evaluate the model on all distortion types. Note that, only scale 4 is supported.
  • train_all_distortions: Train the model on all distortion types. Note that, only scale 4 is supported.
  • precision: The precision of the model's weights. Can be only used when model=edsr.

The result model is stored under weight/ directory.

Baselines

Same training and eval distortion

Model Distortion Downscaling Factor PSNR Precision
EDSR Bicubic 2 31.53 FP32
EDSR Bicubic 4 26.98 FP32
EDSR Mild 4 18.77 FP32
EDSR Difficult 4 19.18 FP32
EDSR Unknown 4 22.85 FP32