Real-time facial restoration and relighting in videos using facial enhancement neural networks
Summary
The USPTO granted Microsoft Technology Licensing, LLC patent US12597096B2 covering an image restoration system that enhances low-quality video images in real-time using neural networks. The technology combines autoencoder models with distortion classifiers to dynamically re-light and restore faces captured under low-light conditions. The patent includes 20 claims.
What changed
The USPTO granted Microsoft patent US12597096B2 on April 7, 2026, covering an image restoration machine-learning model that enhances low-quality video images in real-time. The technology combines autoencoder models with distortion classifiers to dynamically re-light and restore faces captured under poor environmental conditions, with 20 claims covering neural network architectures for image processing and video enhancement.\n\nThis patent grant gives Microsoft exclusive rights to this AI-powered facial restoration technology for video applications. The patent is relevant for video conferencing, surveillance, and media production implementations using neural network-based image enhancement. No immediate compliance actions are required.
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Apr 7, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Real-time facial restoration and relighting in videos using facial enhancement neural networks
Grant US12597096B2 Kind: B2 Apr 07, 2026
Assignee
Microsoft Technology Licensing, LLC
Inventors
Samira Pouyanfar, Sunando Sengupta, Eric Chris Wolfgang Sommerlade, Anjali S. Parikh, Ebey Paulose Abraham, Brian Timothy Hawkins, Mahmoud Mohammadi
Abstract
The present disclosure relates to an image restoration system that efficiently and accurately produces high-quality images captured under low-light and/or low-quality environmental conditions. To illustrate, when a user is in a low-lit environment and participating in a video stream, the image restoration system enhances the quality of the image by dynamically re-lighting the user's face. Moreover, it significantly enhances the image quality to the extent that other users viewing the video stream are unaware of the poor environmental conditions of the user. In addition, the image restoration system creates and utilizes an image restoration machine-learning model to improve the quality of low-quality images by re-lighting and restoring them in real time. Various implementations combine an autoencoder model with a distortion classifier model to create the image restoration machine-learning model.
CPC Classifications
G06T 5/50 G06T 5/70 G06T 5/73 G06T 7/194 G06T 2207/20081 G06T 2207/10016 G06T 2207/20084 G06T 2207/20201 G06T 2207/20208 G06T 2207/30201 G06T 5/90 G06T 5/60 G06T 2207/10024 G06T 2207/10048 G06T 2207/20221 G06T 15/506 G06T 15/80 G06T 3/4046 G06T 9/002 G06T 2207/20076 G06V 40/167 G06V 40/172 G06V 10/30 G06V 10/24 G06V 10/82 G06V 10/143 G06V 30/142 G06V 10/454 G06V 10/54 G06V 10/774 G06V 20/41 G06V 30/18057 G06V 20/698 G06V 30/19173 H04N 23/60 H04N 23/70 H04N 23/73 H04N 23/76 H04N 23/951 H04N 5/265 H04N 9/646 G06N 3/02 G06N 3/08-088 G06N 3/0445 G06N 3/0454 G06N 3/0464 G06N 3/4046 G06N 3/4053 G06N 7/00 G06N 7/01 G06N 20/00 G06K 7/1482 G06F 18/214 G06F 18/22 G06F 18/241 G06F 18/24 G06F 18/2411 G06F 18/2415 G06F 30/27 Y10S 128/925
Filing Date
2023-03-22
Application No.
18188050
Claims
20
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