ARCNN-keras

A tf-keras implementation of ARCNN mentioned in :

• Deep Convolution Networks for Compression Artifacts Reduction, Ke Yu, Chao Dong, Chen Change Loy, Xiaoou Tang

ARCNN-keras network notebook

Requirnments:

• Tensorflow
• tqdm
• Numpy
• Pillow
• Everything within the requirements.txt file.

Dockerfile with all required libs included in repository

Dataset:

The scripts are written to be trained on any folder of images as long as:

1. All images are of the same dimensions
2. All images are of the same file format

example : A folder where all images are pngs and 720p (1280x720)

Recommended that you use Div2k, Use HR or LR based on the closest match to target inference domain.

Models

There are 3 seperate models for training: The ARCNN, Faster ARCNN, ARCNN Lite (A faster ARCNN with dilated convolutions)

The comparision in parameters is given below:

Model	Paramenters
ARCNN	108k
Faster ARCNN	64k
ARCNN Lite	32k

Sample Results

Ground Truth

All outputs are from the dilated model

JPEG 20	Inference
JPEG 15	Inference
JPEG 10	Inference

Usage

Docker

The docker container includes all the packages plus jupyter lab for ease of use. Remember to pass the flag "--ip 0.0.0.0" to jupyter lab

The usage of docker would be the following:

Step 1: Enter repo folder

>cd /location/of/repository

Step 2: Build image from Dockerfile

docker build ./ -t arcnnkeras 

Step 3: Start and enter container

docker run -it --gpus all -v $PWD:/app -p 8888:8888 arcnnkeras bash

Notes:

• Use the "--gpus" flag only is nvidia container runtime is set up
• -v parameters can be added for different data folders that need to be mounted
• The port 8888 is passed for jupyter usage. It isn't needed for inference

Inference

Inside the docker container or in your env use the infer.py script to infer the results on a folder of images.

Folder should follow the rules

1. All images are of the same dimensions
2. All images are of the same file format

usage: infer.py [-h] -p FOLDER_PATH -m MODEL_PATH -o OUTPUT_PATH
optional arguments:
-h, --help show this help message and exit
-p FOLDER_PATH, --folder_path FOLDER_PATH
Path to folder of frames
-m MODEL_PATH, --model_path MODEL_PATH
Path to weights file
-o OUTPUT_PATH, --output_path OUTPUT_PATH
Path to output folder

Example

python infer.py -m ./models/Model2_dialted_epoch/ -p ./test/Inputs/ -o ./test/

• Pre-Trained models are found in the model folder. Link one of them in the -m command. Reccomeneded Model2_dialted_epoch

Training

Use the train_ARCNN.py script in order to train the ARCNN model.

usage: train_ARCNN.py [-h] -m MODEL_SAVE_PATH -c CHECKPOINT_PATH -l
LOG_PATH -d DATASET_PATH [-f FILE_FORMAT] -v
{1,2,3} [-e EPOCHS] [--batch_size BATCH_SIZE]
[--patch_size PATCH_SIZE]
[--stride_size STRIDE_SIZE]
[--jpq_upper JPQ_UPPER] [--jpq_lower JPQ_LOWER]
optional arguments:
-h, --help show this help message and exit
-m MODEL_SAVE_PATH, --model_save_path MODEL_SAVE_PATH
Path to Saved_model
-c CHECKPOINT_PATH, --checkpoint_path CHECKPOINT_PATH
Path to checkpoints
-l LOG_PATH, --log_path LOG_PATH
Path to logdir
-d DATASET_PATH, --dataset_path DATASET_PATH
Path to Folder of images
-f FILE_FORMAT, --file_format FILE_FORMAT
Format of images
-v {1,2,3}, --version {1,2,3}
ARCNN version to train 1: Original | 2: Fast ARCNN |
3: Dilated
-e EPOCHS, --epochs EPOCHS
Number of epochs
--batch_size BATCH_SIZE
Batch size
--patch_size PATCH_SIZE
Patch size for training
--stride_size STRIDE_SIZE
Stride of patches
--jpq_upper JPQ_UPPER
Highest JPEG quality for compression
--jpq_lower JPQ_LOWER
Lowest JPEG quality for compression