# Attention in Attention Network for Image Super-Resolution (A2N)

A2N model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper Attention in Attention Network for Image Super-Resolution by Chen et al. (2021) and first released in this repository.

The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.

## Model description

The A2N model proposes an attention in attention network (A2N) for highly accurate image SR. Specifically, the A2N consists of a non-attention branch and a coupling attention branch. Attention dropout module is proposed to generate dynamic attention weights for these two branches based on input features that can suppress unwanted attention adjustments. This allows attention modules to specialize to beneficial examples without otherwise penalties and thus greatly improve the capacity of the attention network with little parameter overhead.

More importantly the model is lightweight and fast to train (~1.5m parameters, ~4mb).

## Intended uses & limitations

You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.

### How to use

The model can be used with the super_image library:

pip install super-image


Here is how to use a pre-trained model to upscale your image:

from super_image import A2nModel, ImageLoader
from PIL import Image
import requests

url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)

model = A2nModel.from_pretrained('eugenesiow/a2n', scale=2)      # scale 2, 3 and 4 models available
preds = model(inputs)

ImageLoader.save_image(preds, './scaled_2x.png')                        # save the output 2x scaled image to ./scaled_2x.png
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png')      # save an output comparing the super-image with a bicubic scaling


## Training data

The models for 2x, 3x and 4x image super resolution were pretrained on DIV2K, a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).

## Training procedure

### Preprocessing

We follow the pre-processing and training method of Wang et al.. Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times. During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches. Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.

pip install datasets


The following code gets the data and preprocesses/augments the data.

from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop

.map(augment_five_crop, batched=True, desc="Augmenting Dataset")                                # download and augment the data with the five_crop method
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation'))      # prepare the eval dataset for the PyTorch DataLoader


### Pretraining

The model was trained on GPU. The training code is provided below:

from super_image import Trainer, TrainingArguments, A2nModel, A2nConfig

training_args = TrainingArguments(
output_dir='./results',                 # output directory
num_train_epochs=1000,                  # total number of training epochs
)

config = A2nConfig(
scale=4,                                # train a model to upscale 4x
)
model = A2nModel(config)

trainer = Trainer(
model=model,                         # the instantiated model to be trained
args=training_args,                  # training arguments, defined above
train_dataset=train_dataset,         # training dataset
eval_dataset=eval_dataset            # evaluation dataset
)

trainer.train()


## Evaluation results

The evaluation metrics include PSNR and SSIM.

Evaluation datasets include:

The results columns below are represented below as PSNR/SSIM. They are compared against a Bicubic baseline.

Dataset Scale Bicubic A2N
Set5 2x 33.64/0.9292 37.87/0.9602
Set5 3x 30.39/0.8678 34.8/0.9387
Set5 4x 28.42/0.8101 32.07/0.8933
Set14 2x 30.22/0.8683 33.45/0.9162
Set14 3x 27.53/0.7737 30.94/0.8568
Set14 4x 25.99/0.7023 28.56/0.7801
BSD100 2x 29.55/0.8425 32.11/0.8987
BSD100 3x 27.20/0.7382 29.56/0.8173
BSD100 4x 25.96/0.6672 27.54/0.7342
Urban100 2x 26.66/0.8408 31.71/0.9240
Urban100 3x 28.95/0.8671
Urban100 4x 23.14/0.6573 25.89/0.7787

You can find a notebook to easily run evaluation on pretrained models below:

## BibTeX entry and citation info

@misc{chen2021attention,
title={Attention in Attention Network for Image Super-Resolution},
author={Haoyu Chen and Jinjin Gu and Zhi Zhang},
year={2021},
eprint={2104.09497},
archivePrefix={arXiv},
primaryClass={cs.CV}
}