Restoration super-resolution of image sequences : application to TV archive documents

Abstract : The last century has witnessed an explosion in the amount of video data stored with holders such as the National Audiovisual Institute whose mission is to preserve and promote the content of French broadcast programs. The cultural impact of these records, their value is increased due to commercial reexploitation through recent visual media. However, the perceived quality of the old data fails to satisfy the current public demand. The purpose of this thesis is to propose new methods for restoring video sequences supplied from television archive documents, using modern optimization techniques with proven convergence properties. In a large number of restoration issues, the underlying optimization problem is made up with several functions which might be convex and non-necessarily smooth. In such instance, the proximity operator, a fundamental concept in convex analysis, appears as the most appropriate tool. These functions may also involve arbitrary linear operators that need to be inverted in a number of optimization algorithms. In this spirit, we developed a new primal-dual algorithm for computing non-explicit proximity operators based on forward-backward iterations. The proposed algorithm is accelerated thanks to the introduction of a preconditioning strategy and a block-coordinate approach in which at each iteration, only a "block" of data is selected and processed according to a quasi-cyclic rule. This approach is well suited to large-scale problems since it reduces the memory requirements and accelerates the convergence speed, as illustrated by some experiments in deconvolution and deinterlacing of video sequences. Afterwards, a close attention is paid to the study of distributed algorithms on both theoretical and practical viewpoints. We proposed an asynchronous extension of the dual forward-backward algorithm, that can be efficiently implemented on a multi-cores architecture. In our distributed scheme, the primal and dual variables are considered as private and spread over multiple computing units, that operate independently one from another. Nevertheless, communication between these units following a predefined strategy is required in order to ensure the convergence toward a consensus solution. We also address in this thesis the problem of blind video deconvolution that consists in inferring from an input degraded video sequence, both the blur filter and a sharp video sequence. Hence, a solution can be reached by resorting to nonconvex optimization methods that estimate alternatively the unknown video and the unknown kernel. In this context, we proposed a new blind deconvolution method that allows us to implement numerous convex and nonconvex regularization strategies, which are widely employed in signal and image processing
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Feriel Abboud. Restoration super-resolution of image sequences : application to TV archive documents. Image Processing [eess.IV]. Université Paris-Est, 2017. English. ⟨NNT : 2017PESC1038⟩. ⟨tel-02157934⟩

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