LBNet offers a range of use cases for the technical community. For instance, it can be applied in medical imaging to enhance the quality and resolution of scans, providing doctors with clearer and more detailed images for accurate diagnoses. Additionally, LBNet can be employed in surveillance systems to enhance low-resolution footage, aiding in the identification and tracking of individuals or objects of interest. Another practical application could be in satellite or drone imagery, where LBNet can improve the quality of low-resolution images to enable more precise analysis and mapping. Furthermore, LBNet can be integrated into image editing software, allowing users to enhance the resolution and quality of their images with a single click. Other possible products or practical uses of this AI model include video upscaling applications, where LBNet can enhance the resolution and quality of low-resolution videos, and mobile photography apps, where LBNet can enhance the resolution of images taken on smartphone cameras.
- Cost per run
- Avg run time
- Nvidia T4 GPU
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Summary of this model and related resources.
Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric...Read more »
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.00715|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||13 seconds|