Adaptive regularization of the NL-means for video denoising

Abstract : 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.
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Camille Sutour, Jean-François Aujol, Charles-Alban Deledalle, Jean-Philippe Domenger. Adaptive regularization of the NL-means for video denoising. IEEE International Conference on Image Processing 2014, Oct 2014, Paris, France. 5 p. ⟨hal-01016610⟩

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