super-image 0.1.2

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	38.02/0.9607	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
3	a2n	1.0m	37.87/0.9602	33.45/0.9162	32.11/0.8987	31.71/0.9240

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	msrn	6.1m	32.19/0.8951	28.67/0.7833	27.63/0.7374	26.12/0.7866
3	edsr-base	1.5m	32.12/0.8947	28.60/0.7815	27.61/0.7363	26.02/0.7832
4	a2n	1.0m	32.07/0.8933	28.56/0.7801	27.54/0.7342	25.89/0.7787