Deep Learning based Super-Resolution

We propose a method for image super-resolution with basis on deep learning. This method makes use of a convolutional neural network to find the similarities between low-resolution and high-resolution patches of an image and learn a mapping between them. The network is capable of outputting a high-resolution image, taking a low-resolution image as an input, it can handle three color channels, and it’s performant enough for use in real-time systems.