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Visual processing using temporal and spatial interpolation

  • US 10,547,858 B2
  • Filed: 08/17/2017
  • Issued: 01/28/2020
  • Est. Priority Date: 02/19/2015
  • Status: Active Grant
First Claim
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1. A method for upscaling at least a section of low-resolution video data using a convolutional neural network (CNN), the method comprising the steps of:

  • receiving at least three consecutive frames of low-resolution video data;

    inputting the at least three consecutive frames of low-resolution video data into an initial layer of the CNN;

    extracting, using a plurality of hidden convolutional layers of the CNN, low-resolution features from the at least three consecutive frames of low-resolution video data; and

    enhancing, using a hidden convolutional layer of the CNN, the extracted low-resolution features from the three or more consecutive frames of low-resolution video data to generate a higher-resolution target section of video data corresponding to a middle frame of the at least three consecutive frames of low-resolution video data,wherein the CNN is trained on training data including ground truth sections of video data with corresponding sequences of three or more consecutive frames of sub-sampled video data to reproduce ground truth sections of video data from the corresponding frames of sub-sampled video data.

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