Section: New Results
An efficient algorithm for video super–resolution based on a sequential model
Participant : Patrick Héas.
This is a collaboration with Angélique Drémeau (ENSTA Bretagne, Brest) and Cédric Herzet (EPI FLUMINANCE, Inria Rennes–Bretagne Atlantique)
In the work  , we propose a novel procedure for video super–resolution, that is the recovery of a sequence of high–resolution images from its low–resolution counterpart. Our approach is based on a sequential model (i.e. each high–resolution frame is supposed to be a displaced version of the preceding one) and considers the use of sparsity–enforcing priors. Both the recovery of the high–resolution images and the motion fields relating them is tackled. This leads to a large–dimensional, non–convex and non–smooth problem. We propose an algorithmic framework to address the latter. Our approach relies on fast gradient evaluation methods and modern optimization techniques for non–differentiable/non–convex problems. Unlike some other previous works, we show that there exists a provably–convergent method with a complexity linear in the problem dimensions. We assess the proposed optimization method on several video benchmarks and emphasize its good performance with respect to the state of the art.