In this tutorial you will learn how to perform super resolution in images and real-time video streams using OpenCV and Deep Learning.
Today’s blog post is inspired by an email I received from PyImageSearch reader, Hisham:
“Hi Adrian, I read your Deep Learning for Computer Vision with Python book and went through your super resolution implementation with Keras and TensorFlow. It was super helpful, thank you.
I was wondering:
_Are there any pre-trained super resolution models compatible with OpenCV’s _
dnn_ module?Can they work in real-time?_
If you have any suggestions, that would be a big help.”
You’re in luck, Hisham — there are super resolution deep neural networks that are both:
- Pre-trained (meaning you don’t have to train them yourself on a dataset)
- Compatible with OpenCV
**However, OpenCV’s super resolution functionality is actually “hidden” in a submodule named in **
dnn_superres** in an obscure function called ** DnnSuperResImpl_create**.**The function requires a bit of explanation to use, so I decided to author a tutorial on it; that way everyone can learn how to use OpenCV’s super resolution functionality.
By the end of this tutorial, you’ll be able to perform super resolution with OpenCV in both images and real-time video streams!
To learn how to use OpenCV for deep learning-based super resolution, just keep reading.
#opencv #deep learning #python