arbsr

Maintainer: longguangwang

Total Score

21

Last updated 5/21/2024
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Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv

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Model overview

The arbsr model, developed by Longguang Wang, is a plug-in module that extends a baseline super-resolution (SR) network to a scale-arbitrary SR network with a small additional cost. This allows the model to perform non-integer and asymmetric scale factor SR, while maintaining state-of-the-art performance for integer scale factors. This is useful for real-world applications where arbitrary zoom levels are required, beyond the typical integer scale factors.

The arbsr model is related to other SR models like GFPGAN, ESRGAN, SuPeR, and HCFlow-SR, which focus on various aspects of image restoration and enhancement.

Model inputs and outputs

Inputs

  • image: The input image to be super-resolved
  • target_width: The desired width of the output image, which can be 1-4 times the input width
  • target_height: The desired height of the output image, which can be 1-4 times the input width

Outputs

  • Output: The super-resolved image at the desired target size

Capabilities

The arbsr model is capable of performing scale-arbitrary super-resolution, including non-integer and asymmetric scale factors. This allows for more flexible and customizable image enlargement compared to typical integer-only scale factors.

What can I use it for?

The arbsr model can be useful for a variety of real-world applications where arbitrary zoom levels are required, such as image editing, content creation, and digital asset management. By enabling non-integer and asymmetric scale factor SR, the model provides more flexibility and control over the final image resolution, allowing users to zoom in on specific details or adapt the image size to their specific needs.

Things to try

One interesting aspect of the arbsr model is its ability to handle continuous scale factors, which can be explored using the interactive viewer provided by the maintainer. This allows you to experiment with different zoom levels and observe the model's performance in real-time.



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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