PSF Recovery from Examples for Blind Super-Resolution

Abstract

This paper addresses the problem of super-resolving a single image and recovering the characteristics of the sensor using a learning-based approach. In particular, the point spread function (PSF) of the camera is sought by minimizing the mean Euclidean distance function between patches from the input frame and from degraded versions of high-resolution training images. Once an estimate of the PSF is obtained, a supervised learning algorithm can then be used as is. Results are compared with another method for blind super-resolution by using a series of quality measures.