The Experts below are selected from a list of 20085 Experts worldwide ranked by ideXlab platform

M Revnivtsev - One of the best experts on this subject based on the ideXlab platform.

  • a mexican hat with holes calculating low resolution power spectra from data with gaps
    Monthly Notices of the Royal Astronomical Society, 2012
    Co-Authors: P Arevalo, E Churazov, Irina Zhuravleva, C Hernandezmonteagudo, M Revnivtsev
    Abstract:

    A simple method for calculating a low-resolution power spectrum from data with gaps is described. The method is a modification of the Δ-variance method previously described by Stutzki and Ossenkopf. A Mexican hat filter is used to single out fluctuations at a given spatial scale, and the variance of the convolved Image is calculated. The gaps in the Image, defined by the mask, are corrected for by representing the Mexican hat filter as a difference between two Gaussian filters with slightly different widths, convolving the Image and mask with these filters and dividing the results before calculating the final Filtered Image. This method cleanly compensates for data gaps even if these have complicated shapes and cover a significant fraction of the data. The method was developed to deal with problematic 2D Images, where irregular detector edges and masking of contaminating sources compromise the power spectrum estimates, but it can also be straightforwardly applied to 1D timing analysis or 3D data cubes from numerical simulations.

  • a mexican hat with holes calculating low resolution power spectra from data with gaps
    arXiv: Cosmology and Nongalactic Astrophysics, 2012
    Co-Authors: P Arevalo, E Churazov, Irina Zhuravleva, C Hernandezmonteagudo, M Revnivtsev
    Abstract:

    A simple method for calculating a low-resolution power spectrum from data with gaps is described. The method is a modification of the $\Delta$-variance method previously described by Stutzki and Ossenkopf. A Mexican Hat filter is used to single out fluctuations at a given spatial scale and the variance of the convolved Image is calculated. The gaps in the Image, defined by the mask, are corrected for by representing the Mexican Hat filter as a difference between two Gaussian filters with slightly different widths, convolving the Image and mask with these filters and dividing the results before calculating the final Filtered Image. This method cleanly compensates for data gaps even if these have complicated shapes and cover a significant fraction of the data. The method was developed to deal with problematic 2D Images, where irregular detector edges and masking of contaminating sources compromise the power spectrum estimates, but it can also be straightforwardly applied to 1D timing analysis or 3D data cubes from numerical simulations.

Pavel Mrazek - One of the best experts on this subject based on the ideXlab platform.

  • selection of optimal stopping time for nonlinear diffusion filtering
    International Journal of Computer Vision, 2003
    Co-Authors: Pavel Mrazek, Mirko Navara
    Abstract:

    We develop a novel time-selection strategy for iterative Image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the Filtered Image is minimized. The new method is applicable to any Images where the noise to be removed is uncorrelated with the signal, under the assumptions that the filter used is suitable for the given type of data, and that neither the additive noise nor the filtering procedure alter the average gray values no other knowledge (e.g. the noise variance, training data etc.) is needed. We analyse the theoretical properties of the method, then test the performance of our time estimation procedure experimentally, and demonstrate that it yields near-optimal results for a wide range of noise levels and for various filtering methods.

  • selection of optimal stopping time for nonlinear diffusion filtering
    Lecture Notes in Computer Science, 2001
    Co-Authors: Pavel Mrazek
    Abstract:

    We develop a novel time-selection strategy for iterative Image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the Filtered Image is minimised. The new method is applicable to any Images where the noise to be removed is uncorrelated with the signal; no other knowledge (e.g. the noise variance, training data etc.) is needed. We test the performance of our time estimation procedure experimentally, and demonstrate that it yields near-optimal results for a wide range of noise levels and for various filtering methods.

P Arevalo - One of the best experts on this subject based on the ideXlab platform.

  • a mexican hat with holes calculating low resolution power spectra from data with gaps
    Monthly Notices of the Royal Astronomical Society, 2012
    Co-Authors: P Arevalo, E Churazov, Irina Zhuravleva, C Hernandezmonteagudo, M Revnivtsev
    Abstract:

    A simple method for calculating a low-resolution power spectrum from data with gaps is described. The method is a modification of the Δ-variance method previously described by Stutzki and Ossenkopf. A Mexican hat filter is used to single out fluctuations at a given spatial scale, and the variance of the convolved Image is calculated. The gaps in the Image, defined by the mask, are corrected for by representing the Mexican hat filter as a difference between two Gaussian filters with slightly different widths, convolving the Image and mask with these filters and dividing the results before calculating the final Filtered Image. This method cleanly compensates for data gaps even if these have complicated shapes and cover a significant fraction of the data. The method was developed to deal with problematic 2D Images, where irregular detector edges and masking of contaminating sources compromise the power spectrum estimates, but it can also be straightforwardly applied to 1D timing analysis or 3D data cubes from numerical simulations.

  • a mexican hat with holes calculating low resolution power spectra from data with gaps
    arXiv: Cosmology and Nongalactic Astrophysics, 2012
    Co-Authors: P Arevalo, E Churazov, Irina Zhuravleva, C Hernandezmonteagudo, M Revnivtsev
    Abstract:

    A simple method for calculating a low-resolution power spectrum from data with gaps is described. The method is a modification of the $\Delta$-variance method previously described by Stutzki and Ossenkopf. A Mexican Hat filter is used to single out fluctuations at a given spatial scale and the variance of the convolved Image is calculated. The gaps in the Image, defined by the mask, are corrected for by representing the Mexican Hat filter as a difference between two Gaussian filters with slightly different widths, convolving the Image and mask with these filters and dividing the results before calculating the final Filtered Image. This method cleanly compensates for data gaps even if these have complicated shapes and cover a significant fraction of the data. The method was developed to deal with problematic 2D Images, where irregular detector edges and masking of contaminating sources compromise the power spectrum estimates, but it can also be straightforwardly applied to 1D timing analysis or 3D data cubes from numerical simulations.

Mirko Navara - One of the best experts on this subject based on the ideXlab platform.

  • selection of optimal stopping time for nonlinear diffusion filtering
    International Journal of Computer Vision, 2003
    Co-Authors: Pavel Mrazek, Mirko Navara
    Abstract:

    We develop a novel time-selection strategy for iterative Image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the Filtered Image is minimized. The new method is applicable to any Images where the noise to be removed is uncorrelated with the signal, under the assumptions that the filter used is suitable for the given type of data, and that neither the additive noise nor the filtering procedure alter the average gray values no other knowledge (e.g. the noise variance, training data etc.) is needed. We analyse the theoretical properties of the method, then test the performance of our time estimation procedure experimentally, and demonstrate that it yields near-optimal results for a wide range of noise levels and for various filtering methods.

E Churazov - One of the best experts on this subject based on the ideXlab platform.

  • a mexican hat with holes calculating low resolution power spectra from data with gaps
    Monthly Notices of the Royal Astronomical Society, 2012
    Co-Authors: P Arevalo, E Churazov, Irina Zhuravleva, C Hernandezmonteagudo, M Revnivtsev
    Abstract:

    A simple method for calculating a low-resolution power spectrum from data with gaps is described. The method is a modification of the Δ-variance method previously described by Stutzki and Ossenkopf. A Mexican hat filter is used to single out fluctuations at a given spatial scale, and the variance of the convolved Image is calculated. The gaps in the Image, defined by the mask, are corrected for by representing the Mexican hat filter as a difference between two Gaussian filters with slightly different widths, convolving the Image and mask with these filters and dividing the results before calculating the final Filtered Image. This method cleanly compensates for data gaps even if these have complicated shapes and cover a significant fraction of the data. The method was developed to deal with problematic 2D Images, where irregular detector edges and masking of contaminating sources compromise the power spectrum estimates, but it can also be straightforwardly applied to 1D timing analysis or 3D data cubes from numerical simulations.

  • a mexican hat with holes calculating low resolution power spectra from data with gaps
    arXiv: Cosmology and Nongalactic Astrophysics, 2012
    Co-Authors: P Arevalo, E Churazov, Irina Zhuravleva, C Hernandezmonteagudo, M Revnivtsev
    Abstract:

    A simple method for calculating a low-resolution power spectrum from data with gaps is described. The method is a modification of the $\Delta$-variance method previously described by Stutzki and Ossenkopf. A Mexican Hat filter is used to single out fluctuations at a given spatial scale and the variance of the convolved Image is calculated. The gaps in the Image, defined by the mask, are corrected for by representing the Mexican Hat filter as a difference between two Gaussian filters with slightly different widths, convolving the Image and mask with these filters and dividing the results before calculating the final Filtered Image. This method cleanly compensates for data gaps even if these have complicated shapes and cover a significant fraction of the data. The method was developed to deal with problematic 2D Images, where irregular detector edges and masking of contaminating sources compromise the power spectrum estimates, but it can also be straightforwardly applied to 1D timing analysis or 3D data cubes from numerical simulations.