DeepExposure: Xiaomi improves photo exposure through AI

In the last year that is coming to an end we have seen how the biggest mobile phone brands have increasingly focused on the photographic sector of the devices: there has not only been talk of a best camera mounted on the best device, but of algorithms to be used through artificial intelligence on even the most "dated" devices. Xiaomi for example, as we reported here, has focused on the acquisition (in part) of Meitu that he has a lot of beauty algorithms and imaging patents at his disposal; this, coupled with Xiaomi's super competitive prices will surely lead to an improvement in softwareartificial intelligence dedicated to the photographic sector. But today's news is another: following a cross-study "DeepExposure: Learn how to expose photos through antagonistic learning in a reinforced asynchronous way" was originally the Peking University, South China Normal University and Xiaomi technicians we have come to an amazing result. With the DeepExposure, Xiaomi improves the exposure of the photos through AI, without having problems of under and overexposure.

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DeepExposure: Xiaomi improves photo exposure through AI

The researchers of the Xiaomi Lab describe a solution to the exposure dilemma in 'aforementioned article, accepted a Montreal 2018 NeurIPS, an event that takes place from 3 to December 9 this year. This article describes a AI system able to segment the image in more "sub-images", each associated with a particular exposure. The fusion of these under images with different exposures (from below to over-exposed) leads to a photograph that comes very close to the image perceived by the human eye. The researchers said:

"Accurate exposure is key to capturing high-quality photos in computational photography, especially for cell phones that are limited by the size of camera modules.
Inspired by the luminosity masks usually applied by professional photographers, in this article we develop a new algorithm for learning exposures with antagonistic deep reinforcement learning ".

The technique that allows you to execute several instructions in parallel in order to improve the performance of theIA, Nicknamed DeepExposure , starts the image segmentation. Below is a phase in which low-resolution input, sub-images and image fusion are concatenated and processed. After this the algorithm passes to one finishing phase in which one is evaluated the general quality. Finally, the sub-images are mixed up to the final photo. DeepExposureWorking in this way, she managed to restore most of the details and styles in the original images, while improving brightness and colors.

Xiaomi DeepExposure algorithm

To implement this experiment, Xiaomi used the framework TensorFlow open source developed by Google, a series of GPU Nvidia P40 Tesla and a set of images MIT-Adobe FiveK. The innovative method of DeepExposure serves as a bridge between deep-learning methods and traditional methods of filtering: the methods of deep-learning are used to learn filter parameters, which makes the filtering of traditional methods more precise. Traditional methods reduce training time deep-learning methods because pixel filtering is much faster than that of new technologies.

For Xiaomi, after the acquisition of Meitu's algorithms and beauty filters, sparks are expected for the photographic sector. Will we come to an era where mirrorless and SLRs will no longer be needed? What do you think about it? Write it to us in the comments




Young Chinese language student with the dream of being immersed in the world of technology. The passion for language and for the Middle Country brought me to xiaomitoday.it. Mail: [Email protected]om

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