Learning nonlinear spectral filters for color image reconstruction

Moeller M, Diebold J, Gilboa G, Cremers D (2015)


Publication Type: Conference contribution

Publication year: 2015

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2015 International Conference on Computer Vision, ICCV 2015

Pages Range: 289-297

Conference Proceedings Title: Proceedings of the IEEE International Conference on Computer Vision

Event location: Santiago, CHL

ISBN: 9781467383912

DOI: 10.1109/ICCV.2015.41

Abstract

This paper presents the idea of learning optimal filters for color image reconstruction based on a novel concept of nonlinear spectral image decompositions recently proposed by Guy Gilboa. We use a multiscale image decomposition approach based on total variation regularization and Bregman iterations to represent the input data as the sum of image layers containing features at different scales. Filtered images can be obtained by weighted linear combinations of the different frequency layers. We introduce the idea of learning optimal filters for the task of image denoising, and propose the idea of mixing high frequency components of different color channels. Our numerical experiments demonstrate that learning the optimal weights can significantly improve the results in comparison to the standard variational approach, and achieves state-of-the-art image denoising results.

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How to cite

APA:

Moeller, M., Diebold, J., Gilboa, G., & Cremers, D. (2015). Learning nonlinear spectral filters for color image reconstruction. In Proceedings of the IEEE International Conference on Computer Vision (pp. 289-297). Santiago, CHL: Institute of Electrical and Electronics Engineers Inc..

MLA:

Moeller, Michael, et al. "Learning nonlinear spectral filters for color image reconstruction." Proceedings of the 15th IEEE International Conference on Computer Vision, ICCV 2015, Santiago, CHL Institute of Electrical and Electronics Engineers Inc., 2015. 289-297.

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