super-image 0.1.1

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.

Scale x2

Rank	Model	Params	Set5	Set14	BSD100	Urban100
1	msrn-bam	5.9m	38.02/0.9607	33.63/0.9177	32.20/0.8998	32.08/0.9276
2	edsr-base	1.5m	38.02/0.9607	33.57/0.9172	32.21/0.8999	32.04/0.9276

Scale x3

Rank	Model	Params	Set5	Set14	BSD100	Urban100
1	msrn-bam	5.9m	35.16/0.9410	30.97/0.8574	29.67/0.8209	29.31/0.8737
2	edsr-base	1.5m	35.04/0.9403	30.93/0.8567	29.65/0.8204	29.23/0.8723

Scale x4

Rank	Model	Params	Set5	Set14	BSD100	Urban100
1	msrn-bam	5.9m	32.26/0.8955	28.66/0.7829	27.61/0.7369	26.10/0.7857
2	edsr-base	1.5m	32.12/0.8947	28.60/0.7815	27.61/0.7363	26.02/0.7832
3	a2n	1.0m	32.07/0.8933	28.56/0.7801	27.54/0.7342	25.89/0.7787