Super resolution using a generative adversarial network

Abstract

A neural network is trained to process received visual data to estimate a high-resolution version of the visual data using a training dataset and reference dataset. A set of training data is generated, and a generator convolutional neural network parameterized by first weights and biases is trained by comparing characteristics of the training data to characteristics of the reference dataset. The first network is trained to generate super-resolved image data from low-resolution image data and the training includes modifying first weights and biases to optimize processed visual data based on the comparison between the characteristics of the training data and the characteristics of the reference dataset. A discriminator convolutional neural network parameterized by second weights and biases is trained by comparing characteristics of the generated super-resolved image data to characteristics of the reference dataset, and where the second network is trained to discriminate super-resolved image data from real image data.

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