swin2sr
Maintainer: mv-lab
3.5K
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
swin2sr is a state-of-the-art AI model for photorealistic image super-resolution and restoration, developed by the mv-lab research team. It builds upon the success of the SwinIR model by incorporating the novel Swin Transformer V2 architecture, which improves training convergence and performance, especially for compressed image super-resolution tasks.
The model outperforms other leading solutions in classical, lightweight, and real-world image super-resolution, JPEG compression artifact reduction, and compressed input super-resolution. It was a top-5 solution in the "AIM 2022 Challenge on Super-Resolution of Compressed Image and Video".
Similar models in the image restoration and enhancement space include supir, stable-diffusion, instructir, gfpgan, and seesr.
Model inputs and outputs
swin2sr takes low-quality, low-resolution JPEG compressed images as input and generates high-quality, high-resolution images as output. The model can upscale the input by a factor of 2, 4, or other scales, depending on the task.
Inputs
- Low-quality, low-resolution JPEG compressed images
Outputs
- High-quality, high-resolution images with reduced compression artifacts and enhanced visual details
Capabilities
swin2sr can effectively tackle various image restoration and enhancement tasks, including:
- Classical image super-resolution
- Lightweight image super-resolution
- Real-world image super-resolution
- JPEG compression artifact reduction
- Compressed input super-resolution
The model's excellent performance is achieved through the use of the Swin Transformer V2 architecture, which improves training stability and data efficiency compared to previous transformer-based approaches like SwinIR.
What can I use it for?
swin2sr can be particularly useful in applications where image quality and resolution are crucial, such as:
- Enhancing images for high-resolution displays and printing
- Improving image quality for streaming services and video conferencing
- Restoring old or damaged photos
- Generating high-quality images for virtual reality and gaming
The model's ability to handle compressed input super-resolution makes it a valuable tool for efficient image and video transmission and storage in bandwidth-limited systems.
Things to try
One interesting aspect of swin2sr is its potential to be used in combination with other image processing and generation models, such as instructir or stable-diffusion. By integrating swin2sr into a workflow that starts with text-to-image generation or semantic-aware image manipulation, users can achieve even more impressive and realistic results.
Additionally, the model's versatility in handling various image restoration tasks makes it a valuable tool for researchers and developers working on computational photography, low-level vision, and image signal processing applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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