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In a new advancement, an AI system developed by a team of researchers allows you to erase all the sound of a photo. What about pixilation, watermarks or even text. This new AI image restoration technique also improves granular images, and this new method was found by people at Nvidia, MIT, and Aalto University. This AI system is powered by a deep learning neural network that has been trained with the help of 50,000 photos, to get an idea of what a noiseless final result should ideally look like. The system "has learned to repair photos by simply looking at examples of corrupt photos only."
The research report submitted by the team suggests that the AI engine can restore images better than a professional photo restorer. The researchers included in this project are Jaako Lehtinen, Jacob Munkberg, Jon Hbadelgren, Samuli Laine, Tero Karras, Miika Maittala and Timo Aila. They used the Nvidia Tesla P100 GPUs with the cuDNN accelerated TensorFlow deep learning framework, and trained their system on 50,000 images in the ImageNet Validation Set. They have never shown the system what an image looks like without noise, and even without image shaping before and after, this AI can remove artifacts, noise, grain and automatically enhance photos. Although this does not require a clean image for learning purposes, it needs to see the source image twice before making the changes.
"Without ever showing what an image without sound looks like, this AI can remove artifacts, noise, grain, and automatically enhance your photos," reads a blog.
"There are several real-world situations where getting clean workout data is difficult: low light photography (eg, astronomical imaging), physical rendering and magnetic resonance imaging. Our Demonstrations Proof of Concept open the way to significant potential benefits in these applications by removing the need for a potentially painstaking collection of clean data. free lunch – we can not learn to find features that are not there in the input data – but this is also true for training with own goals, "the team wrote in his paper.
AI The system has many real-world applications, as it can be used to enhance MRI images, perhaps pave the way for the improvement of medical imaging.
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