Super-Resolution

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Super-resolution is an open-source Tensorflow 2.x implementation for single image super-resolution, based on EDSR, WDSR, and SRGAN models. It allows developers and researchers to enhance image quality by increasing resolution.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is super-resolution?

This open-source project provides a Tensorflow 2.x based implementation of state-of-the-art models for single image super-resolution, including Enhanced Deep Residual Networks (EDSR), Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR), and Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN). It offers a high-level training API, enabling users to train models as described in the respective papers and fine-tune EDSR and WDSR models within an SRGAN context. The tool includes a DIV2K data provider for automatic dataset downloads and offers pre-trained weights for quick setup. It's ideal for developers and researchers working on image processing and computer vision tasks.

Best used for

Ideal for developers and researchers who need to implement and experiment with advanced single image super-resolution techniques, train custom models, and evaluate their performance. Especially valuable for academic research, computer vision projects, and developing image enhancement applications.

Common actions

implement super-resolution models

train image enhancement models

research image processing

upscale images

face swappinggithub copilotcollaborationlow-code/no-codeopen-sourceautomated workflowworkflows"AI Agents"deepfake

Capabilities

Key features

EDSR model implementation
WDSR model implementation
SRGAN model implementation
High-level training API
DIV2K data provider
Pre-trained model weights
Fine-tune EDSR/WDSR with SRGAN

Target Audience

developersresearchersmachine learning engineerscomputer vision scientists

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning frameworks does this project use?

This project is built on Tensorflow 2.x, providing implementations of EDSR, WDSR, and SRGAN models. It leverages Tensorflow's capabilities for efficient training and deployment of super-resolution networks.

Can I use my own dataset for training with this tool?

While the project includes a DIV2K data provider, the training API is designed to be flexible. You can adapt the data loading mechanisms to integrate your custom datasets for training the EDSR, WDSR, or SRGAN models.

Are there pre-trained models available for immediate use?

Yes, the project provides pre-trained weights for EDSR, WDSR, and SRGAN models. These weights allow users to quickly test the super-resolution capabilities without needing to train models from scratch.

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Coding & Development › Open Source & Models Research & Education › Scientific Computing

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