Nonparmetric Blind Super-Resolution
Tomer Michaeli and Michal Irani
This webpage presents the paper "Nonparametric blind super-resolution" (ICCV 2013).
Abstract
Super resolution (SR) algorithms typically assume that
the blur kernel is known (either the Point Spread Function
‘PSF’ of the camera, or some default low-pass filter, e.g. a
Gaussian). However, the performance of SR methods significantly
deteriorates when the assumed blur kernel deviates
from the true one. We propose a general framework
for “blind” super resolution. In particular, we show that:
(i) Unlike the common belief, the PSF of the camera is the
wrong blur kernel to use in SR algorithms. (ii) We show how
the correct SR blur kernel can be recovered directly from
the low-resolution image. This is done by exploiting the inherent
recurrence property of small natural image patches
(either internally within the same image, or externally in a
collection of other natural images). In particular, we show
that recurrence of small patches across scales of the low-res
image (which forms the basis for single-image SR), can also
be used for estimating the optimal blur kernel. This leads to
significant improvement in SR results.
1. Paper [PDF]
2. More examples and results [PPT]
