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Journal Articles SIAM Journal on Imaging Sciences Year : 2016

An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model

Abstract

In this 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.
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Dates and versions

hal-01158551 , version 1 (01-06-2015)
hal-01158551 , version 2 (18-12-2015)
hal-01158551 , version 3 (15-02-2016)

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Patrick Héas, Angélique Drémeau, Cédric Herzet. An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model. SIAM Journal on Imaging Sciences, 2016, 9 (2), pp.537-572. ⟨10.1137/15M1023956⟩. ⟨hal-01158551v3⟩
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