BasicSR

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BasicSR is an open-source image and video restoration toolbox for super-resolution, denoising, and deblurring. It supports various models including EDSR, RCAN, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, and ECBSR, as well as StyleGAN2 and DFDNet.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is BasicSR?

BasicSR (Basic Super Restoration) is an open-source image and video restoration toolbox built on PyTorch. It provides a comprehensive suite of functionalities for tasks such as super-resolution, denoising, deblurring, and JPEG artifact removal. The toolbox integrates a wide array of state-of-the-art models, including EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, and ECBSR, offering flexibility for various restoration needs. Additionally, it supports advanced models like StyleGAN2 for image generation and DFDNet for face restoration. BasicSR is designed to be a foundational platform for researchers and developers working on image and video enhancement, offering both training and inference capabilities for its included models.

Best used for

Ideal for researchers and developers who need to implement and experiment with advanced image and video restoration techniques, enhance visual content, and remove common image imperfections. Especially valuable for those working with super-resolution, denoising, and deblurring models in a PyTorch environment.

Common actions

restore images

enhance videos

denoise photos

deblur images

upscale resolution

github copilot"AI Agents"face swappingcollaborationdeepfakeworkflowsopen-sourcelow-code/no-codeautomated workflow

Capabilities

Key features

Image super-resolution
Video super-resolution
Image denoising
Image deblurring
JPEG artifact removal
StyleGAN2 support
DFDNet support

Target Audience

ai researchersmachine learning engineerscomputer vision developersacademics

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of image and video restoration tasks does BasicSR support?

BasicSR supports a wide range of image and video restoration tasks, including super-resolution, denoising, deblurring, and the removal of JPEG compression artifacts. It provides implementations for various state-of-the-art models to address these challenges effectively.

Which deep learning models are included in the BasicSR toolbox?

The BasicSR toolbox includes popular models such as EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, and ECBSR for restoration tasks. It also extends support to models like StyleGAN2 for image generation and DFDNet for face restoration.

Is BasicSR suitable for both training and inference of models?

Yes, BasicSR is designed to facilitate both the training and inference of deep learning models for image and video restoration. It provides the necessary framework and code examples to help users get started with their research and development quickly.

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