The Experts below are selected from a list of 1722 Experts worldwide ranked by ideXlab platform

A.k. Katsaggelos - One of the best experts on this subject based on the ideXlab platform.

  • EUSIPCO - Maximum a posteriori super-resolution of compressed video with a novel Multichannel Image prior and a new observation model
    2011
    Co-Authors: Stefanos P. Belekos, Nikolaos P. Galatsanos, A.k. Katsaggelos
    Abstract:

    In this paper we propose a class of SR algorithms for compressed video using the maximum a posteriori (MAP) approach. These algorithms utilize a novel Multichannel Image prior model which has already been presented mainly for uncompressed video, along with a new hierarchical Gaussian nonstationary version of the state-of-the-art quantization noise model. The relationship between model components and the decoded bitstream is also demonstrated. An additional novelty of this framework pertains to the transition flexibility from totally nonstationary algorithms used for compressed video to fully stationary algorithms used for raw video. Numerical simulations comparing the proposed models among themselves, verify the efficacy of the adopted Multichannel nonstationary prior for different compression ratios, and the significant role of the nonstationary observation term.

  • Maximum a posteriori super-resolution of compressed video with a novel Multichannel Image prior and a new observation model
    2011 19th European Signal Processing Conference, 2011
    Co-Authors: Stefanos P. Belekos, Nikolaos P. Galatsanos, A.k. Katsaggelos
    Abstract:

    In this paper we propose a class of SR algorithms for compressed video using the maximum a posteriori (MAP) approach. These algorithms utilize a novel Multichannel Image prior model which has already been presented mainly for uncompressed video, along with a new hierarchical Gaussian nonstationary version of the state-of-the-art quantization noise model. The relationship between model components and the decoded bitstream is also demonstrated. An additional novelty of this framework pertains to the transition flexibility from totally nonstationary algorithms used for compressed video to fully stationary algorithms used for raw video. Numerical simulations comparing the proposed models among themselves, verify the efficacy of the adopted Multichannel nonstationary prior for different compression ratios, and the significant role of the nonstationary observation term.

  • Maximum a Posteriori Video Super-Resolution Using a New Multichannel Image Prior
    IEEE Transactions on Image Processing, 2010
    Co-Authors: Stefanos P. Belekos, Nikolaos P. Galatsanos, A.k. Katsaggelos
    Abstract:

    Super-resolution (SR) is the term used to define the process of estimating a high-resolution (HR) Image or a set of HR Images from a set of low-resolution (LR) observations. In this paper we propose a class of SR algorithms based on the maximum a posteriori (MAP) framework. These algorithms utilize a new Multichannel Image prior model, along with the state-of-the-art single channel Image prior and observation models. A hierarchical (two-level) Gaussian nonstationary version of the Multichannel prior is also defined and utilized within the same framework. Numerical experiments comparing the proposed algorithms among themselves and with other algorithms in the literature, demonstrate the advantages of the adopted Multichannel approach.

  • ICIP - Maximum a posteriori super-resolution of compressed video using a new Multichannel Image prior
    2009 16th IEEE International Conference on Image Processing (ICIP), 2009
    Co-Authors: Stefanos P. Belekos, Nikolaos P. Galatsanos, S. Derin Babacan, A.k. Katsaggelos
    Abstract:

    Super-resolution (SR) algorithms for compressed video aim at recovering high-frequency information and estimating a high-resolution (HR) Image or a set of HR Images from a sequence of low-resolution (LR) video frames. In this paper we present a novel SR algorithm for compressed video based on the maximum a posteriori (MAP) framework. We utilize a new Multichannel Image prior model, along with the state-of-the art Image prior and observation models. Moreover, relationship between model parameters and the decoded bitstream are established. Numerical experiments demonstrate the improved performance of the proposed method compared to existing algorithms for different compression ratios.

  • Maximum a posteriori super-resolution of compressed video using a new Multichannel Image prior
    2009 16th IEEE International Conference on Image Processing (ICIP), 2009
    Co-Authors: Stefanos P. Belekos, Nikolaos P. Galatsanos, Derin S. Babacan, A.k. Katsaggelos
    Abstract:

    Super-resolution (SR) algorithms for compressed video aim at recovering high-frequency information and estimating a high-resolution (HR) Image or a set of HR Images from a sequence of low-resolution (LR) video frames. In this paper we present a novel SR algorithm for compressed video based on the maximum a posteriori (MAP) framework. We utilize a new Multichannel Image prior model, along with the state-of-the art Image prior and observation models. Moreover, relationship between model parameters and the decoded bitstream are established. Numerical experiments demonstrate the improved performance of the proposed method compared to existing algorithms for different compression ratios.

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

  • Multichannel Image processing using Fuzzy Vector Median-rational hybrid filters
    2000 10th European Signal Processing Conference, 2000
    Co-Authors: L. Khriji, M. Gabbouj
    Abstract:

    A new Multichannel filtering approach is introduced and analyzed in this paper. These filters are based on rational functions (RF) using fuzzy transformations of the Euclidean distances among the different vectors to adapt to local data in the Image. The output is the result of vector rational operation taking into account three sub-functions, such as two Fuzzy Vector Median (FVM) sub-filters and one Fuzzy Center Weighted Vector Median Filter (FCWVMF). Simulation studies indicate that the filters are computationally attractive and have excellent performance such as edge and details preservation and accurate chromaticity estimation.

  • Class of Multichannel Image processing filters
    Electronics Letters, 1999
    Co-Authors: L. Khriji, M. Gabbouj
    Abstract:

    A new class of Multichannel Image processing filters called vector median rational hybrid filters (VMRHFs) for multispectral Image processing is introduced and applied to the colour Image filtering problem. These filters are based on rational functions. The rational function structure is attractive in filtering since it is a universal approximator and a good extrapolator. The VMRHF is a two stage filter, which exploits the features of the vector median filter and those of the vector rational operator. These filters exhibit desirable properties such as edge and detail preservation and accurate chromaticity estimation.

  • Vector median-rational hybrid filters for Multichannel Image processing
    IEEE Signal Processing Letters, 1999
    Co-Authors: L. Khriji, M. Gabbouj
    Abstract:

    In this letter, a new class of nonlinear filters called vector median-rational hybrid filters (VMRHFs) for multispectral Image processing is introduced and applied to the color Image filtering problem. These filters are based on rational functions (RFs) offering a number of advantages. First, a rational function is a universal approximator and a good extrapolator. Second, it can be trained by a linear adaptive algorithm. Third, it produces the best approximation (w.r.t. a given cost function) for some specific functions. The output is the result of a vector rational operation over the output of three subfilters, such as two vector median (VM) subfilters and one center weighted vector median filter (CWVMF). These filters exhibit desirable properties, such as edge and details preservation and accurate chromaticity estimation.

  • Vector median-rational hybrid filters for Multichannel Image processing
    1999 IEEE International Symposium on Circuits and Systems (ISCAS), 1999
    Co-Authors: L. Khriji, M. Gabbouj
    Abstract:

    In this paper, a new class of nonlinear filters called Vector Median Rational Hybrid Filters (VMRHF) for multispectral Image processing was introduced and applied to the color Image filtering problem. These filters are based on Rational Functions (RF). The VMRHF is a two-stage filter, which exploits the features of the vector median filter (VM) and those of the vector rational operator (VRF) (The output is the result of vector rational operation taking into account three sub-functions, such as two vector median sub-filters and one center weighted vector median filter (CWVMF)). These filters exhibit desirable properties, such as, edge and details preservation and accurate chromaticity estimation. The performances of the proposed filter are compared with those of the vector median and the directional-distance filters (DDF).

Florian Luisier - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP - SURE-LET Multichannel Image denoising: undecimated wavelet thresholding
    2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008
    Co-Authors: Florian Luisier
    Abstract:

    We propose an extension of the recently devised SURE-LET grayscale denoising approach for Multichannel Images. Assuming additive Gaussian white noise, the unknown linear parameters of a transform-domain/wwfwwe Multichannel thresholding are globally optimized by minimizing Stein's unbiased MSE estimate (SURE) in the Image-domain. Using the undecimated wavelet transform, we demonstrate the efficiency of this approach for denoising color Images by comparing our results with two other state-of-the-art denoising algorithms.

  • SURE-LET Multichannel Image denoising: undecimated wavelet thresholding
    2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008
    Co-Authors: Florian Luisier
    Abstract:

    We propose an extension of the recently devised SURE-LET grayscale denoising approach for Multichannel Images. Assuming additive Gaussian white noise, the unknown linear parameters of a transform-domain/wwfwwe Multichannel thresholding are globally optimized by minimizing Stein's unbiased MSE estimate (SURE) in the Image-domain. Using the undecimated wavelet transform, we demonstrate the efficiency of this approach for denoising color Images by comparing our results with two other state-of-the-art denoising algorithms.

  • SURE-LET Multichannel Image Denoising: Interscale Orthonormal Wavelet Thresholding
    IEEE Transactions on Image Processing, 2008
    Co-Authors: Florian Luisier
    Abstract:

    We propose a vector/matrix extension of our denoising algorithm initially developed for grayscale Images, in order to efficiently process Multichannel (e.g., color) Images. This work follows our recently published SURE-LET approach where the denoising algorithm is parameterized as a linear expansion of thresholds (LET) and optimized using Stein's unbiased risk estimate (SURE). The proposed wavelet thresholding function is pointwise and depends on the coefficients of same location in the other channels, as well as on their parents in the coarser wavelet subband. A nonredundant, orthonormal, wavelet transform is first applied to the noisy data, followed by the (subband-dependent) vector-valued thresholding of individual Multichannel wavelet coefficients which are finally brought back to the Image domain by inverse wavelet transform. Extensive comparisons with the state-of-the-art multiresolution Image denoising algorithms indicate that despite being nonredundant, our algorithm matches the quality of the best redundant approaches, while maintaining a high computational efficiency and a low CPU/memory consumption. An online Java demo illustrates these assertions.

A.k. Katsaggelos - One of the best experts on this subject based on the ideXlab platform.

  • Multichannel Image restoration using compound Gauss-Markov random fields
    2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000
    Co-Authors: R. Molina, J. Mateos, A.k. Katsaggelos
    Abstract:

    A solution to the Multichannel Image restoration problem is provided using compound Gauss-Markov random fields. For the single channel deblurring problem the convergence of the simulated annealing (SA) and iterative conditional mode (ICM) algorithms has not been established. We propose two new iterative Multichannel restoration algorithms which can be considered as extensions of the classical SA and ICM approaches and whose convergence is established. Experimental results with color Images demonstrate the effectiveness of the proposed algorithms.

  • A regularized mixed norm Multichannel Image restoration approach
    Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381), 1998
    Co-Authors: M.-c. Hong, T. Stathaki, A.k. Katsaggelos
    Abstract:

    We develop a deterministic regularized mixed norm Multichannel Image restoration algorithm. A functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional using both within- and between-channel deterministic information is proposed. One parameter is defined to control the relative contribution between the LMS and the LMF norms, and a second one (regularization parameter) is defined to control the degree of smoothness of the solution. They are both updated at each iteration step. The novelty of the proposed algorithm is that no knowledge about the noise distribution for each channel is required, and the parameters are adjusted based on the partially restored Image.

  • Simultaneous Multichannel Image restoration and estimation of the regularization parameters
    IEEE Transactions on Image Processing, 1997
    Co-Authors: Moon Gi Kang, A.k. Katsaggelos
    Abstract:

    In this correspondence, a constrained least-squares Multichannel Image restoration approach is proposed, in which no prior knowledge of the noise variance at each channel or the degree of smoothness of the original Image is required. The regularization functional for each channel is determined by incorporating both within-channel and cross-channel information. It is shown that the proposed smoothing functional has a global minimizer.

R. Molina - One of the best experts on this subject based on the ideXlab platform.