super-image 0.1.3

State-of-the-art image super resolution models for PyTorch.

super-image

State-of-the-art image super resolution models for PyTorch.

Requirements

super-image requires Python 3.6 or above.

Installation

With pip:

pip install super-image

Quick Start

Quickly utilise pre-trained models for upscaling your images 2x, 3x and 4x. See the full list of models below.

from super_image import EdsrModel, 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 = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
inputs = ImageLoader.load_image(image)
preds = model(inputs)

ImageLoader.save_image(preds, './scaled_2x.png')
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png')

Pre-trained Models

Pre-trained models are available at various scales and hosted at the awesome huggingface_hub. By default the models 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).

The leaderboard below shows the PSNR / SSIM metrics for each model at various scales on various test sets (Set5, Set14, BSD100, Urban100). The higher the better. All training was to 1000 epochs (some publications, like a2n, train to >1000 epochs in their experiments).

Scale x2

Rank	Model	Params	Set5	Set14	BSD100	Urban100
1	msrn-bam	5.9m	38.02/0.9608	33.73/0.9186	33.78/0.9253	32.08/0.9276
2	edsr-base	1.5m	38.02/0.9607	33.66/0.9180	33.77/0.9254	32.04/0.9276
3	a2n	1.0m	37.87/0.9602	33.54/0.9171	33.67/0.9244	31.71/0.9240

Scale x3

Rank	Model	Params	Set5	Set14	BSD100	Urban100
1	msrn-bam	5.9m	35.13/0.9408	31.06/0.8588	29.65/0.8196	29.26/0.8736
2	edsr-base	1.5m	35.01/0.9402	31.01/0.8583	29.63/0.8190	29.19/0.8722

Scale x4

Rank	Model	Params	Set5	Set14	BSD100	Urban100
1	msrn	6.1m	32.19/0.8951	28.78/0.7862	28.53/0.7657	26.12/0.7866
2	msrn-bam	5.9m	32.26/0.8955	28.78/0.7859	28.51/0.7651	26.10/0.7857
3	edsr-base	1.5m	32.12/0.8947	28.72/0.7845	28.50/0.7644	26.02/0.7832
4	a2n	1.0m	32.07/0.8933	28.68/0.7830	28.44/0.7624	25.89/0.7787