Artificial Intelligence system can turn low-resolution images into HD versions

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Berlin | Published: October 29, 2017 4:02:55 PM

Scientists have developed an artificial intelligence (AI) system that can create a high- definition version of a low resolution image.

artificial intelligence photo resolution, artificial intelligence picture resolution, picture resolution artificial intelligenceThe team applied artificial intelligence and an adaptive algorithm for upsampling the image learns from experience to improve the result. (Representative Image: Reuters)

Scientists have developed an artificial intelligence (AI) system that can create a high- definition version of a low resolution image. Technology to create a large-sized image from a low- resolution image is known as single-image super-resolution (SISR) technology. SISR has been studied for decades, but with limited results. Software adds extra pixels and averages them with the surrounding pixels, but the result is blurriness. Researchers at the Max Planck Institute of Intelligent Systems in Germany proposed a new approach to give images a realistic texture when magnified from small to large using machine learning. The team applied artificial intelligence and an adaptive algorithm for upsampling the image learns from experience to improve the result. The learning process is much like that of a human, researchers said. “The algorithm is given the task of upsampling millions of low-resolution images to a high-resolution version, and is then shown the original,” said Mehdi MS Sajjadi from Max Planck Institute of Intelligent Systems. Researchers developed the EnhanceNet-PAT technology that once trained, no longer needs the original photos.

The technology is more efficient than any other SISR technology currently on the market. In contrast to existing algorithms, EnhanceNet-PATdoes not attempt pixel-perfect reconstruction, but rather aims for faithful texture synthesis, researchers said.

By detecting and generating patterns in a low-resolution image and applying these patterns in the upsampling process, EnhanceNet-PAT adds extra pixels to the low-resolution image accordingly, they said. For most viewers, the result is very similar the original photo, researchers added.

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