The Experts below are selected from a list of 1830 Experts worldwide ranked by ideXlab platform

Yicong Zhou - One of the best experts on this subject based on the ideXlab platform.

  • quaternion non local total variation for Color Image Denoising
    Systems Man and Cybernetics, 2019
    Co-Authors: Yicong Zhou, Jing Zhang
    Abstract:

    Many existing Color Image Denoising methods process Color channels individually and fail to consider their cross-channel correlations. To solve this problem, in this paper, we employ the quaternion representation of the Color Image and propose a novel Quaternion Non-local Total Variation (QNLTV) model to remove Gaussian noise from Color Images. We first introduce the coupled quaternion distance to measure the Color Image patch similarity. Decomposing the Color Image into brightness and chromaticity components in quaternion domain, we then divide the QNLTV model into two quaternion optimizaiton problems and solve them alternatively. Experiment results show that QNLTV has the significantly better Denoising performance than competing methods in terms of visual and quantitative evaluations.

  • Color Image Denoising using quaternion adaptive non local coupled means
    International Conference on Image Processing, 2019
    Co-Authors: Yicong Zhou, Jing Zhang
    Abstract:

    Because quaternion representation is able to preserve the relationship among RGB channels of a Color Image, we take advantage of this characteristic and propose a new Color Image Denoising method, named the Quaternion Adaptive Non-local Coupled Means (QANLCM). QANLCM first builds a four-variable optimization model by decomposing the original noisy Image into a luminance component and a chromaticity component, and then alternatively updates the four variables in each Denoising iteration. Simulations and comparisons demonstrate that QANLCM shows a superiority to other non-local means methods in removing Gaussian noise from Color Images.

  • ICIP - Color Image Denoising Using Quaternion Adaptive Non-Local Coupled Means
    2019 IEEE International Conference on Image Processing (ICIP), 2019
    Co-Authors: Yicong Zhou, Jing Zhang
    Abstract:

    Because quaternion representation is able to preserve the relationship among RGB channels of a Color Image, we take advantage of this characteristic and propose a new Color Image Denoising method, named the Quaternion Adaptive Non-local Coupled Means (QANLCM). QANLCM first builds a four-variable optimization model by decomposing the original noisy Image into a luminance component and a chromaticity component, and then alternatively updates the four variables in each Denoising iteration. Simulations and comparisons demonstrate that QANLCM shows a superiority to other non-local means methods in removing Gaussian noise from Color Images.

  • SMC - Quaternion Non-local Total Variation for Color Image Denoising
    2019 IEEE International Conference on Systems Man and Cybernetics (SMC), 2019
    Co-Authors: Yicong Zhou, Jing Zhang
    Abstract:

    Many existing Color Image Denoising methods process Color channels individually and fail to consider their cross-channel correlations. To solve this problem, in this paper, we employ the quaternion representation of the Color Image and propose a novel Quaternion Non-local Total Variation (QNLTV) model to remove Gaussian noise from Color Images. We first introduce the coupled quaternion distance to measure the Color Image patch similarity. Decomposing the Color Image into brightness and chromaticity components in quaternion domain, we then divide the QNLTV model into two quaternion optimizaiton problems and solve them alternatively. Experiment results show that QNLTV has the significantly better Denoising performance than competing methods in terms of visual and quantitative evaluations.

  • total variation regularized low rank tensor approximation for Color Image Denoising
    Systems Man and Cybernetics, 2018
    Co-Authors: Yongyong Chen, Yicong Zhou
    Abstract:

    Existing approaches for low-rank approximation either need a rank prior or ignore the spatial smooth characteristic of a Color Image. To overcome these drawbacks, we propose a total variation regularized low-rank tensor approximation model for Color Image Denoising. The model integrates the strong low-rank prior into a tensor-SVD framework, and introduces the hyper total variation to model the spatial smooth structure of Images. Using the alternating direction method of multipliers, we propose a simple algorithm to solve our model. Extensive results on simulated and real noisy Color Images demonstrate the better performance of the proposed method against state-of-the-art Denoising methods.

Lan Tang - One of the best experts on this subject based on the ideXlab platform.

  • weighted t schatten p norm minimization for real Color Image Denoising
    IEEE Access, 2020
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    In this paper, to fully exploit the spatial and spectral correlation information, we present a new real Color Image Denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0 <; p <; 1) norm minimization could obtain better results than the tensor nuclear norm minimization which is a convex relaxation of the nonconvex tensor tubal rank. To avoid over-shrink the tensor tubal rank components, a flexible weighted t-Schatten-p norm model is proposed with weights assigned to different elements of tensor singular tubes. We adopt the generalized iterated shrinkage algorithm to solve the minimization problem efficiently. Extensive experiments on one synthetic and two realistic datasets demonstrate the effectiveness of our proposed method to remove noise both quantitatively and qualitatively.

  • Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising
    IEEE Access, 2020
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    In this paper, to fully exploit the spatial and spectral correlation information, we present a new real Color Image Denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0

  • Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
    IEEE Access, 2019
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    Color Images can be seen as third-order tensors with column, row and Color modes. Considering two inherent characteristics of a Color Image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar patch groups from a Color Image and treat these groups as tensors with each Color channel corresponding to the frontal slice of the tensor to exploit the information within and cross channel correlation. Inspired by recently proposed tensor-tensor product (t-product), t-SVD, tensor tubal rank and rigorously deduced tensor nuclear norm, a novel t-product based weighted tensor nuclear norm minimization (WTNNM) is proposed to model the extracted non-local similar patch group tensor (NPGT). Considering the NPGT is of low tubal rank, we formulate real Color Image Denoising as a low tubal rank tensor recovery problem and solve it with the weighted tensor nuclear norm minimization. Experiments on both simulated and realistic noisy Images verify the effectiveness of our method.

Min Liu - One of the best experts on this subject based on the ideXlab platform.

  • weighted t schatten p norm minimization for real Color Image Denoising
    IEEE Access, 2020
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    In this paper, to fully exploit the spatial and spectral correlation information, we present a new real Color Image Denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0 <; p <; 1) norm minimization could obtain better results than the tensor nuclear norm minimization which is a convex relaxation of the nonconvex tensor tubal rank. To avoid over-shrink the tensor tubal rank components, a flexible weighted t-Schatten-p norm model is proposed with weights assigned to different elements of tensor singular tubes. We adopt the generalized iterated shrinkage algorithm to solve the minimization problem efficiently. Extensive experiments on one synthetic and two realistic datasets demonstrate the effectiveness of our proposed method to remove noise both quantitatively and qualitatively.

  • Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising
    IEEE Access, 2020
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    In this paper, to fully exploit the spatial and spectral correlation information, we present a new real Color Image Denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0

  • Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
    IEEE Access, 2019
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    Color Images can be seen as third-order tensors with column, row and Color modes. Considering two inherent characteristics of a Color Image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar patch groups from a Color Image and treat these groups as tensors with each Color channel corresponding to the frontal slice of the tensor to exploit the information within and cross channel correlation. Inspired by recently proposed tensor-tensor product (t-product), t-SVD, tensor tubal rank and rigorously deduced tensor nuclear norm, a novel t-product based weighted tensor nuclear norm minimization (WTNNM) is proposed to model the extracted non-local similar patch group tensor (NPGT). Considering the NPGT is of low tubal rank, we formulate real Color Image Denoising as a low tubal rank tensor recovery problem and solve it with the weighted tensor nuclear norm minimization. Experiments on both simulated and realistic noisy Images verify the effectiveness of our method.

Yongyong Chen - One of the best experts on this subject based on the ideXlab platform.

  • total variation regularized low rank tensor approximation for Color Image Denoising
    Systems Man and Cybernetics, 2018
    Co-Authors: Yongyong Chen, Yicong Zhou
    Abstract:

    Existing approaches for low-rank approximation either need a rank prior or ignore the spatial smooth characteristic of a Color Image. To overcome these drawbacks, we propose a total variation regularized low-rank tensor approximation model for Color Image Denoising. The model integrates the strong low-rank prior into a tensor-SVD framework, and introduces the hyper total variation to model the spatial smooth structure of Images. Using the alternating direction method of multipliers, we propose a simple algorithm to solve our model. Extensive results on simulated and real noisy Color Images demonstrate the better performance of the proposed method against state-of-the-art Denoising methods.

  • SMC - Total Variation Regularized Low-Rank Tensor Approximation for Color Image Denoising
    2018 IEEE International Conference on Systems Man and Cybernetics (SMC), 2018
    Co-Authors: Yongyong Chen, Yicong Zhou
    Abstract:

    Existing approaches for low-rank approximation either need a rank prior or ignore the spatial smooth characteristic of a Color Image. To overcome these drawbacks, we propose a total variation regularized low-rank tensor approximation model for Color Image Denoising. The model integrates the strong low-rank prior into a tensor-SVD framework, and introduces the hyper total variation to model the spatial smooth structure of Images. Using the alternating direction method of multipliers, we propose a simple algorithm to solve our model. Extensive results on simulated and real noisy Color Images demonstrate the better performance of the proposed method against state-of-the-art Denoising methods.

Xinggan Zhang - One of the best experts on this subject based on the ideXlab platform.

  • weighted t schatten p norm minimization for real Color Image Denoising
    IEEE Access, 2020
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    In this paper, to fully exploit the spatial and spectral correlation information, we present a new real Color Image Denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0 <; p <; 1) norm minimization could obtain better results than the tensor nuclear norm minimization which is a convex relaxation of the nonconvex tensor tubal rank. To avoid over-shrink the tensor tubal rank components, a flexible weighted t-Schatten-p norm model is proposed with weights assigned to different elements of tensor singular tubes. We adopt the generalized iterated shrinkage algorithm to solve the minimization problem efficiently. Extensive experiments on one synthetic and two realistic datasets demonstrate the effectiveness of our proposed method to remove noise both quantitatively and qualitatively.

  • Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising
    IEEE Access, 2020
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    In this paper, to fully exploit the spatial and spectral correlation information, we present a new real Color Image Denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor. Similar to matrix Schatten-p norm, using non-convex t-Schatten-p (0

  • Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
    IEEE Access, 2019
    Co-Authors: Min Liu, Xinggan Zhang, Lan Tang
    Abstract:

    Color Images can be seen as third-order tensors with column, row and Color modes. Considering two inherent characteristics of a Color Image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar patch groups from a Color Image and treat these groups as tensors with each Color channel corresponding to the frontal slice of the tensor to exploit the information within and cross channel correlation. Inspired by recently proposed tensor-tensor product (t-product), t-SVD, tensor tubal rank and rigorously deduced tensor nuclear norm, a novel t-product based weighted tensor nuclear norm minimization (WTNNM) is proposed to model the extracted non-local similar patch group tensor (NPGT). Considering the NPGT is of low tubal rank, we formulate real Color Image Denoising as a low tubal rank tensor recovery problem and solve it with the weighted tensor nuclear norm minimization. Experiments on both simulated and realistic noisy Images verify the effectiveness of our method.