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--- |
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license: apache-2.0 |
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tags: |
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- super-image |
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- image-super-resolution |
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datasets: |
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- eugenesiow/Div2k |
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- eugenesiow/Set5 |
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- eugenesiow/Set14 |
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- eugenesiow/BSD100 |
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- eugenesiow/Urban100 |
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metrics: |
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- pnsr |
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- ssim |
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--- |
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# Densely Residual Laplacian Super-Resolution (DRLN) |
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DRLN 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 [Densely Residual Laplacian Super-resolution](https://arxiv.org/abs/1906.12021) by Anwar et al. (2020) and first released in [this repository](https://github.com/saeed-anwar/DRLN). |
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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 and model upscaling. |
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## Model description |
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Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images. However, existing algorithms often require very deep architectures and long training times. Furthermore, current convolutional neural networks for super-resolution are unable to exploit features at multiple scales and weigh them equally, limiting their learning capability. In this exposition, we present a compact and accurate super-resolution algorithm namely, Densely Residual Laplacian Network (DRLN). The proposed network employs cascading residual on the residual structure to allow the flow of low-frequency information to focus on learning high and mid-level features. In addition, deep supervision is achieved via the densely concatenated residual blocks settings, which also helps in learning from high-level complex features. Moreover, we propose Laplacian attention to model the crucial features to learn the inter and intra-level dependencies between the feature maps. Furthermore, comprehensive quantitative and qualitative evaluations on low-resolution, noisy low-resolution, and real historical image benchmark datasets illustrate that our DRLN algorithm performs favorably against the state-of-the-art methods visually and accurately. |
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## Intended uses & limitations |
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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. |
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### How to use |
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The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library: |
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```bash |
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pip install super-image |
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``` |
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Here is how to use a pre-trained model to upscale your image: |
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```python |
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from super_image import DrlnModel, ImageLoader |
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from PIL import Image |
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import requests |
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url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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model = DrlnModel.from_pretrained('eugenesiow/drln', scale=2) # scale 2, 3 and 4 models available |
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inputs = ImageLoader.load_image(image) |
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preds = model(inputs) |
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ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png` |
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ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling |
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``` |
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[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab") |
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## Training data |
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The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co./datasets/eugenesiow/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). |
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## Training procedure |
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### Preprocessing |
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We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566). |
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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. |
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During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches. |
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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. |
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We need the huggingface [datasets](https://huggingface.co./datasets?filter=task_ids:other-other-image-super-resolution) library to download the data: |
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```bash |
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pip install datasets |
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``` |
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The following code gets the data and preprocesses/augments the data. |
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```python |
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from datasets import load_dataset |
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from super_image.data import EvalDataset, TrainDataset, augment_five_crop |
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augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\ |
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.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method |
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train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader |
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eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader |
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``` |
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### Pretraining |
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The model was trained on GPU. The training code is provided below: |
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```python |
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from super_image import Trainer, TrainingArguments, DrlnModel, DrlnConfig |
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training_args = TrainingArguments( |
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output_dir='./results', # output directory |
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num_train_epochs=1000, # total number of training epochs |
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) |
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config = DrlnConfig( |
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scale=4, # train a model to upscale 4x |
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) |
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model = DrlnModel(config) |
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trainer = Trainer( |
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model=model, # the instantiated model to be trained |
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args=training_args, # training arguments, defined above |
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train_dataset=train_dataset, # training dataset |
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eval_dataset=eval_dataset # evaluation dataset |
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) |
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trainer.train() |
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``` |
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[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab") |
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## Evaluation results |
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The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm). |
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Evaluation datasets include: |
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- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co./datasets/eugenesiow/Set5) |
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- Set14 - [Zeyde et al. (2010)](https://huggingface.co./datasets/eugenesiow/Set14) |
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- BSD100 - [Martin et al. (2001)](https://huggingface.co./datasets/eugenesiow/BSD100) |
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- Urban100 - [Huang et al. (2015)](https://huggingface.co./datasets/eugenesiow/Urban100) |
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The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline. |
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|Dataset |Scale |Bicubic |drln | |
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|--- |--- |--- |--- | |
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|Set5 |2x |33.64/0.9292 |**38.22/0.9614** | |
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|Set5 |3x |30.39/0.8678 |**35.31/0.9423** | |
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|Set5 |4x |28.42/0.8101 |**32.55/0.899** | |
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|Set14 |2x |30.22/0.8683 |**34.01/0.9211** | |
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|Set14 |3x |27.53/0.7737 |**31.21/0.8619** | |
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|Set14 |4x |25.99/0.7023 |**28.96/0.7901** | |
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|BSD100 |2x |29.55/0.8425 |**33.93/0.9269** | |
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|BSD100 |3x |27.20/0.7382 |**29.77/0.8223** | |
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|BSD100 |4x |25.96/0.6672 |**28.65/0.7692** | |
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|Urban100 |2x |26.66/0.8408 |**32.82/0.934** | |
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|Urban100 |3x | |**29.79/0.8825** | |
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|Urban100 |4x |23.14/0.6573 |**26.56/0.7998** | |
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You can find a notebook to easily run evaluation on pretrained models below: |
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[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab") |
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## BibTeX entry and citation info |
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```bibtex |
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@misc{anwar2019densely, |
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title={Densely Residual Laplacian Super-Resolution}, |
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author={Saeed Anwar and Nick Barnes}, |
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year={2019}, |
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eprint={1906.12021}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.IV} |
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} |
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``` |
