From Image Super-Resolution to Face Hallucination
Single image super-resolution is a classical problem in computer vision. It aims at recovering a high-resolution image from a single low-resolution image. This problem is an underdetermined inverse problem, of which solution is not unique. In this seminar I will share our efforts in solving the problem by deep convolutional networks in a data-driven manner. I will then discuss our recent work on hallucinating faces of unconstrained poses and with very low resolution. In particular, I will show how face hallucination and dense correspondence field estimation can be optimized in a unified deep network. I will also introduce our ongoing effort in developing deep networks for other low-level image processing tasks.