refvsr-cvpr2022
Maintainer: codeslake
14
| 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
refvsr-cvpr2022 is a reference-based video super-resolution model developed by codeslake and presented at CVPR 2022. It improves on existing video super-resolution approaches by using a reference wide-angle video frame to super-resolve an ultra-wide low-resolution video frame. This is useful for scenarios where a wide-angle and ultra-wide camera are available, like in action cameras or smartphones. Similar models include lcm-video2video, which uses a latent consistency model for fast video-to-video translation, and arbsr, which can perform scale-arbitrary super-resolution.
Model inputs and outputs
refvsr-cvpr2022 takes two inputs: a low-resolution ultra-wide video frame (LR) and a reference wide-angle video frame (Ref). It outputs the super-resolved ultra-wide video frame (SR_output), using the reference frame to guide the super-resolution process.
Inputs
- LR: Low-resolution ultra-wide video frame to super-resolve
- Ref: Reference wide-angle video frame
Outputs
- SR_output: Super-resolved ultra-wide video frame
Capabilities
refvsr-cvpr2022 can perform high-quality 4x video super-resolution, significantly improving the resolution of ultra-wide video frames by leveraging a wide-angle reference frame. This enables applications like high-resolution action cameras or smartphone videos without the need for expensive ultra-wide sensors.
What can I use it for?
The refvsr-cvpr2022 model can be used to enhance the quality of ultra-wide video footage, such as from action cameras or smartphones, by super-resolving the frames using a reference wide-angle video. This can be useful for content creators, video production companies, or anyone looking to improve the resolution of their ultra-wide video without expensive hardware upgrades.
Things to try
One interesting thing to try with refvsr-cvpr2022 is exploring the impact of the reference frame quality and field of view on the super-resolution performance. The model's ability to leverage the reference frame is a key aspect, so experimenting with different reference video conditions could yield insights into the model's capabilities and limitations.
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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