To create the ML Super Resolution feature, we used a convolutional neural network. This type of deep neural network reduces raster images and their complex inter-pixel dependencies into a form that is easier to process (i.e. requires less computation) without losing important features (edges, patterns, colors, textures, gradients, and so on). The ML Super Resolution network includes 29 convolutional layers which scan the image and create an over-100-channel-deep version of it that contains a range of identified features. This is then upscaled, post-processed and turned back into a raster image. Below is a simplified representation of the neural network.

Not quite all about it, and there’s better references for the technique, but neat to see this trickle down to entry level photo editing.

source: DF