SUTOUR, Camille; AUJOL, Jean-François; DELEDALLE, Charles-Alban; DOMENGER, Jean-Philippe

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SUTOUR, Camille
Institut de Mathématiques de Bordeaux [IMB]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

AUJOL, Jean-François
Institut de Mathématiques de Bordeaux [IMB]

DELEDALLE, Charles-Alban
Institut de Mathématiques de Bordeaux [IMB]

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IEEE International Conference on Image Processing 2014, IEEE International Conference on Image Processing 2014, IEEE International Conference on Image Processing 2014, 2014-10-27, Paris. 2014-10-27p. 5 p.

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We derive a denoising method based on an adaptive regularization of the non-local means. The NL-means reduce noise by using the redundancy in natural images. They compute a weighted average of pixels whose surroundings are close. This method performs well but it suffers from residual noise on singular structures. We use the weights computed in the NL-means as a measure of performance of the denoising process. These weights balance the data-fidelity term in an adapted ROF model, in order to locally perform adaptive TV regularization. Besides, this model can be adapted to different noise statistics and a fast resolution can be computed in the general case of the exponential family. We adapt this model to video denoising by using spatio-temporal patches. Compared to spatial patches, they offer better temporal stability, while the adaptive TV regularization corrects the residual noise observed around moving structures.< Réduire

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Adaptive regularization of the NL-means for video denoising

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