lbnet
Maintainer: wzx0826
7
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The lbnet model is a Lightweight Bimodal Network for Single-Image Super-Resolution (SISR) developed by wzx0826. It uses a symmetric CNN and recursive transformer to efficiently upscale images while maintaining high visual quality. This model builds upon prior work in SISR, such as EDSR and DRN, to provide a better tradeoff between performance, model size, and inference speed.
Model inputs and outputs
Inputs
- image: The image to be upscaled, provided as a URI
- variant: The specific model variant to use, defaulting to
LBNet-X2 - rgb_range: The RGB range of the input image, defaulting to 255
- max_img_width: The maximum width of the input image in pixels, defaulting to 400
- max_img_height: The maximum height of the input image in pixels, defaulting to 400
Outputs
- The upscaled image, provided as a URI
Capabilities
The lbnet model is capable of efficiently upscaling images while preserving high-quality details and textures. It achieves this through a unique architecture that combines a symmetric CNN and a recursive transformer. This allows the model to capture both local and global information effectively, resulting in superior performance compared to traditional SISR approaches.
What can I use it for?
The lbnet model can be useful for a variety of image enhancement and restoration tasks, such as:
- Upscaling low-resolution images for high-quality displays or printing
- Enhancing the quality of images captured by mobile devices or low-end cameras
- Improving the visual fidelity of AI-generated images or old photographs
By leveraging the model's efficient design and high-quality output, you can incorporate it into applications that require advanced image processing capabilities.
Things to try
One interesting aspect of the lbnet model is its ability to balance performance, model size, and inference speed. You could experiment with different variants of the model, such as LBNet-T or LBNet-X4, to find the best tradeoff for your specific use case. Additionally, you could try integrating the model into your own image processing pipelines to see how it performs on your unique datasets and requirements.
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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