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Communication Dans Un Congrès Année : 2015

Non-parametric Ensemble Kalman methods for the inpainting of noisy dynamic textures

Résumé

In this work, we propose a novel non parametric method for the temporally consistent inpainting of dynamic texture sequences. The inpainting of texture image sequences is stated as a stochastic assimilation issue, for which a novel model-free and data-driven Ensemble Kalman method is introduced. Our model is inspired by the Analog Ensemble Kalman Filter (AnEnKF) recently proposed for the assimilation of geophysical space-time dynamics, where the physical model is replaced by the use of statistical analogs or nearest neighbours. Such a non-parametric framework is of key interest for image processing applications, as prior models are seldom available in general. We present experimental evidence for real dynamic texture that using only a catalog database of historical data and without having any assumption on the model, the proposed method provides relevant dynamically-consistent interpolation and outperforms the classical parametric (autoregressive) dynamical prior.
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Dates et versions

hal-01271173 , version 1 (08-02-2016)

Identifiants

Citer

Redouane Lguensat, Pierre Tandeo, Ronan Fablet, Pierre Ailliot. Non-parametric Ensemble Kalman methods for the inpainting of noisy dynamic textures. ICIP 2015 : IEEE International Conference on Image Processing, Sep 2015, Québec City, Canada. pp.2488-2492, ⟨10.1109/ICIP.2015.7351615⟩. ⟨hal-01271173⟩
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