This Face Super-Resolution model from the Korea Advanced Institute of Science and Technology (KAIST) is meant to improve resolution of low quality facial images. The model takes 16 x 16 pixel pictures of human faces as input and scales the resolution up to 128 x 128 pixels. The results are impressive albeit with some strange artifacts (see below). Because this model is trained specifically to look for facial landmarks it will take any excuse to draw eyes and nostrils on a pixel…These samples are cherry picked and many outputs were not very interesting. I also went to some lengths to encourage the model to make aggressive guesses about facial features.

Progressive Face Super-Resolution via Attention to Facial Landmark arxiv.org is a machine learning model trained to reconstruct face images from tiny 16 16 pixel input images, scaling them up to 128 128 with nearly photo-realistic results. I tried running emojis, Twitch emotes, and a few game sprites through it. I did have to do quite a fit of cherry picking, and I also iteratively ran the output back into the inputs to encourage the model the add human features. I also created a (very sloppy) Colab Version of the paper's github demo if you want to try this yourself.