The single image super-resolution problem involves estimating a high-resolution image from a single, low-resolution observation. Due to its highly ill-posed nature, the choice of appropriate priors has been an active research area of late. Data driven or learning based priors have been successful in addressing this problem. In this talk, I will review some recent learning based approaches to the super-resolution problem, and present some novel algorithms which can better super-resolve high-frequency details in the scene. In articular, I will talk about novel self-similarity driven algorithms that do not require any external database of training images, but instead, learn the mapping from low-resolution to high-resolution using patch recurrence across scales,within the same image. Furthermore, I will also present a novel framework for jointly/simultaneously addressing the super-resolution and denoising problems, in order to obtain a clean, high-resolution image from a single, noise corrupted, low-resolution observation.

Abhishek Singh is a Research Scientist at Amazon Lab126 in Sunnyvale, California. He obtained a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in Feb 2015, where he worked with Prof. Narendra Ahuja on learning based super-resolution algorithms, among other problems. He was the recipient of the Joan and Lalit Bahl Fellowship, and the Computational Science and Engineering Fellowship at the University of Illinois. He has also been affiliated with Mitsubishi Electric Research Labs, Siemens Corporate Research, and UtopiaCompression Corporation. His current research interests include learning based approaches for low level vision and image processing problems. For more information, please visit http://www.abhishek486.com