swinir

Maintainer: jingyunliang

Total Score

5.7K

Last updated 6/13/2024
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Model overview

swinir is an image restoration model based on the Swin Transformer architecture, developed by researchers at ETH Zurich. It achieves state-of-the-art performance on a variety of image restoration tasks, including classical image super-resolution, lightweight image super-resolution, real-world image super-resolution, grayscale and color image denoising, and JPEG compression artifact reduction. The model is trained on diverse datasets like DIV2K, Flickr2K, and OST, and outperforms previous state-of-the-art methods by up to 0.45 dB while reducing the parameter count by up to 67%.

Model inputs and outputs

swinir takes in an image and performs various image restoration tasks. The model can handle different input sizes and scales, and supports tasks like super-resolution, denoising, and JPEG artifact reduction.

Inputs

  • Image: The input image to be restored.
  • Task type: The specific image restoration task to be performed, such as classical super-resolution, lightweight super-resolution, real-world super-resolution, grayscale denoising, color denoising, or JPEG artifact reduction.
  • Scale factor: The desired upscaling factor for super-resolution tasks.
  • Noise level: The noise level for denoising tasks.
  • JPEG quality: The JPEG quality factor for JPEG artifact reduction tasks.

Outputs

  • Restored image: The output image with the requested restoration applied, such as a high-resolution, denoised, or JPEG artifact-reduced version of the input.

Capabilities

swinir is capable of performing a wide range of image restoration tasks with state-of-the-art performance. For example, it can take a low-resolution, noisy, or JPEG-compressed image and output a high-quality, clean, and artifact-free version. The model works well on a variety of image types, including natural scenes, faces, and text-heavy images.

What can I use it for?

swinir can be used in a variety of applications that require high-quality image restoration, such as:

  • Enhancing the resolution and quality of low-quality images for use in social media, e-commerce, or photography.
  • Improving the visual fidelity of images generated by GFPGAN or Codeformer for better face restoration.
  • Reducing noise and artifacts in images captured in low-light or poor conditions for better visualization and analysis.
  • Preprocessing images for downstream computer vision tasks like object detection or classification.

Things to try

One interesting thing to try with swinir is using it to restore real-world images that have been degraded by various factors, such as low resolution, noise, or JPEG artifacts. The model's ability to handle diverse degradation types and produce high-quality results makes it a powerful tool for practical image restoration applications.

Another interesting experiment would be to compare swinir's performance to other state-of-the-art image restoration models like SuperPR or Swin2SR on a range of benchmark datasets and tasks. This could help understand the relative strengths and weaknesses of the different approaches.



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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