Thanks to the AI of Nvidia you can say bye bye to watermarks on photos

They all know how annoying are the brands of water that have some pictures, well hinder the illustration of a text, and although it is well known that the artist made the work, there is nothing good for anyone that has this limitation.

Thinking of that and how to fix it, a group of researchers from Nvidia along with the MIT and the Aalto University, created Noise2Noise, an Artificial Intelligence system that may remove all of the components of identification that have a photograph.

With this new tool, the company is planning to enter the field of photography, and although this is not a direct relationship with the image capture, yes this is a feature we all wanted to find and use.

In addition to this, there will be a way to improve the pictures, because it will eliminate the noise and grain that you possess, since not always it is possible to handle environmental conditions that cause these defects, and that once in a while ruin the shots.

Noise2Noise has a neural network of deep learning, which was fueled by nearly 50,000 images taken from ImageNet, scans mris and some other computer-generated.

The point of showing so many images was that you’ll notice the difference between the original and the that contained extra elements that made it look “bad”, because, this way it would be able to piece it together until it had no noise, grain, and/or water marks.

The remove the items that are not needed, it will help the photos astronomical and magnetic resonance imaging, to be more clear as to what they contain, to avoid confusion about some new discovery with some spot without a sense.

Soon you will be able to convert videos to normal to slow motion with new technology from NVIDIA

Through this document, it explains the way in which it works, the AI and as have plans to include it in the same cameras using a software that serves to improve the photo immediately.

To make official its launch, the team work will take you to the International Conference of Machine Learning Stockholm, in Sweden to be known by all.

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