Super resolution
Remember the typical scene in a crime series when they have a blurry image of a suspect and ask their technology expert to “zoom in and enhance”?

Although those scenes are nowhere technically accurate, there exist some techniques that take low-resolution images as input and upscale them to higher resolution ones. Super-resolution is one of them and for a long time the idea was thought to be science fiction as the “data processing inequality theorem” states that the post-processing of data cannot add any information that was not already there. However, with the advent of neural networks and GAN’s, you can add information that was learned by training these networks on large amounts of examples thus allowing for actual reconstruction of faces for example.

Super-resolution has a lot of interesting real-world applications that are only just starting to be explored, such as reducing the file sizes of images and videos, as a preprocessing step for various AI applications such as for example deepfakes and as a post-processing step in various industries such as in the medical field, cosmology or simply for enhancing your favorite old movies and pictures.

While there is growing interest, super resolution still faces major challenges in developing effective algorithms.

--> Image colorization
--> Video colorization
--> Garment transfer
--> Conditional GAN's
--> Z-space feature disentanglement