Hi,

Image Super Resolution is an umbrella term for generating a high-resolution image from its low-resolution state. The traditional super resolution methods are severely constrained. But once deep learning comes into the fray, you can make your model learn salient features from images such that they retain every bit of information when upscaled. 

Image

Super Resolution using deep learning is now used for various computer vision tasks. For example, the Academy Awards recently used it to preserve old and critically acclaimed films by digitizing them and using deep learning techniques to improve their quality. 

The big picture: Teach your model to learn features from low-resolution images such that the information is fully retained while the image is upscaled. 

How it works: In our technique, we are feeding low-resolution images as input and setting up their corresponding high-resolution versions as output to make the model learn a one-to-one correlation.

Our thoughts: This tutorial serves as a brilliant entry to the world of Image Super Resolution using deep learning.

Yes, but: Several better and more efficient deep learning architectures have already been developed, which outperform our architecture used today. 

Stay smart: Do not stop at just this tutorial. Instead, let this be the starting point of your deep dive into the world of Image Super Resolution. 

Click here to read the full tutorial

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