SRGAN

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SRGAN is an open-source PyTorch implementation of SRGAN for photo-realistic single image super-resolution. It enhances image and video resolution using a Generative Adversarial Network.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is SRGAN?

SRGAN is a PyTorch implementation of the Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network paper from CVPR 2017. This open-source tool allows users to perform super-resolution on both images and videos, significantly enhancing their quality and detail. It provides options for various upscale factors (2x, 4x, 8x) and includes benchmarks for performance on different datasets. Users can train their own models, test on benchmark datasets, or apply super-resolution to single images and videos using pre-trained models. The project is hosted on GitHub and requires Anaconda, PyTorch, and OpenCV for setup.

Best used for

Ideal for photographers and graphic designers who need to enhance the resolution of low-quality images, upscale videos for better clarity, and experiment with different super-resolution factors. Especially valuable for researchers and developers interested in implementing and customizing GAN-based super-resolution models.

Common actions

upscale images

enhance video resolution

improve image quality

train super-resolution models

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Capabilities

Key features

Image super-resolution
Video super-resolution
Multiple upscale factors
Custom model training
Benchmark testing

Target Audience

photographergraphic designer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements to run SRGAN?

To run SRGAN, you need to have Anaconda, PyTorch, and OpenCV installed. The project is a PyTorch implementation, so a compatible environment with these libraries is essential for training and testing the models.

Can SRGAN be used to upscale videos?

Yes, SRGAN supports video super-resolution. You can use the provided scripts to test low-resolution video files and generate super-resolved versions, with options to specify the upscale factor and model.

What datasets are used for training and testing SRGAN?

The training and validation datasets are sampled from VOC2012. Test image datasets include Set 5, Set 14, BSD 100, Sun-Hays 80, and Urban 100. Test video datasets consist of three trailers.

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Coding & Development › Open Source & Models

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