The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform

Luis Miguel Gomez Diaz - One of the best experts on this subject based on the ideXlab platform.

  • optimum time frequency representations of Monocomponent Signal combinations
    Signal Processing, 1994
    Co-Authors: Adrian T Perez, Jose Restrepo, Luis Miguel Gomez Diaz
    Abstract:

    Abstract In this paper the objective is to select, based on the ambiguity function, the optimum kernel or nucleus to represent linear combinations of Monocomponent Signals in the time-frequency domain. The nucleus should be optimum in the sense that it preserves the relevant information without loss of resolution, eliminating the cross-terms and, as far as possible, any impurities that affect the Signal. The analytical development of the proposed method is presented together with experiments which demonstrate its effectiveness.

N. Pustelnik - One of the best experts on this subject based on the ideXlab platform.

  • Proximal Algorithms for Multicomponent Image Recovery Problems
    Journal of Mathematical Imaging and Vision, 2011
    Co-Authors: L. M. Briceño-arias, P. L. Combettes, J.-c. Pesquet, N. Pustelnik
    Abstract:

    In recent years, proximal splitting algorithms have been applied to various Monocomponent Signal and image recovery problems. In this paper, we address the case of multicomponent problems. We first provide closed form expressions for several important multicomponent proximity operators and then derive extensions of existing proximal algorithms to the multicomponent setting. These results are applied to stereoscopic image recovery, multispectral image denoising, and image decomposition into texture and geometry components.

  • Proximal Algorithms for Multicomponent Image Recovery Problems
    Journal of Mathematical Imaging and Vision, 2010
    Co-Authors: L. M. Briceño-arias, P. L. Combettes, J.-c. Pesquet, N. Pustelnik
    Abstract:

    International audienceIn recent years, proximal splitting algorithms have been applied to various Monocomponent Signal and image recovery problems. In this paper, we address the case of multicomponent problems. We first provide closed form expressions for several important multicomponent proximity operators and then derive extensions of existing proximal algorithms to the multicomponent setting. These results are applied to stereoscopic image recovery, multispectral image denoising, and image decomposition into texture and geometry components

Adrian T Perez - One of the best experts on this subject based on the ideXlab platform.

  • optimum time frequency representations of Monocomponent Signal combinations
    Signal Processing, 1994
    Co-Authors: Adrian T Perez, Jose Restrepo, Luis Miguel Gomez Diaz
    Abstract:

    Abstract In this paper the objective is to select, based on the ambiguity function, the optimum kernel or nucleus to represent linear combinations of Monocomponent Signals in the time-frequency domain. The nucleus should be optimum in the sense that it preserves the relevant information without loss of resolution, eliminating the cross-terms and, as far as possible, any impurities that affect the Signal. The analytical development of the proposed method is presented together with experiments which demonstrate its effectiveness.

L. M. Briceño-arias - One of the best experts on this subject based on the ideXlab platform.

  • Proximal Algorithms for Multicomponent Image Recovery Problems
    Journal of Mathematical Imaging and Vision, 2011
    Co-Authors: L. M. Briceño-arias, P. L. Combettes, J.-c. Pesquet, N. Pustelnik
    Abstract:

    In recent years, proximal splitting algorithms have been applied to various Monocomponent Signal and image recovery problems. In this paper, we address the case of multicomponent problems. We first provide closed form expressions for several important multicomponent proximity operators and then derive extensions of existing proximal algorithms to the multicomponent setting. These results are applied to stereoscopic image recovery, multispectral image denoising, and image decomposition into texture and geometry components.

  • Proximal Algorithms for Multicomponent Image Recovery Problems
    Journal of Mathematical Imaging and Vision, 2010
    Co-Authors: L. M. Briceño-arias, P. L. Combettes, J.-c. Pesquet, N. Pustelnik
    Abstract:

    International audienceIn recent years, proximal splitting algorithms have been applied to various Monocomponent Signal and image recovery problems. In this paper, we address the case of multicomponent problems. We first provide closed form expressions for several important multicomponent proximity operators and then derive extensions of existing proximal algorithms to the multicomponent setting. These results are applied to stereoscopic image recovery, multispectral image denoising, and image decomposition into texture and geometry components

Jose Restrepo - One of the best experts on this subject based on the ideXlab platform.

  • optimum time frequency representations of Monocomponent Signal combinations
    Signal Processing, 1994
    Co-Authors: Adrian T Perez, Jose Restrepo, Luis Miguel Gomez Diaz
    Abstract:

    Abstract In this paper the objective is to select, based on the ambiguity function, the optimum kernel or nucleus to represent linear combinations of Monocomponent Signals in the time-frequency domain. The nucleus should be optimum in the sense that it preserves the relevant information without loss of resolution, eliminating the cross-terms and, as far as possible, any impurities that affect the Signal. The analytical development of the proposed method is presented together with experiments which demonstrate its effectiveness.