The Experts below are selected from a list of 87663 Experts worldwide ranked by ideXlab platform

Karen Panetta - One of the best experts on this subject based on the ideXlab platform.

  • wavelet transform coefficient histogram based image enhancement algorithms
    Proceedings of SPIE, 2010
    Co-Authors: Junjun Xia, Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the spatial method of histogram equalization with the logarithmic transform domain coefficient Histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • Image enhancement based on transform coefficient histogram shifting and shaping
    2010 IEEE International Conference on Technologies for Homeland Security (HST), 2010
    Co-Authors: Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the spatial method of histogram equalization with the wavelet transform domain coefficient Histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • logarithmic transform coefficient histogram matching with spatial equalization
    Visual Information Processing Conference, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    In this paper we propose an image enhancement algorithm that is based on utilizing histogram data gathered from transform domain coefficients that will improve on the limitations of the histogram equalization method. Traditionally, classical histogram equalization has had some problems due to its inherent dynamic range expansion. Many images with data tightly clustered around certain intensity values can be over enhanced by standard histogram equalization, leading to artifacts and overall tonal change of the image. In the transform domain, one has control over subtle image properties such as low and high frequency content with their respective magnitudes and phases. However, due to the nature of many of these transforms, the coefficient’s Histograms may be so tightly packed that distinguishing them from one another may be impossible. By placing the transform coefficients in the logarithmic transform domain, it is easy to see the difference between different quality levels of images based upon their logarithmic transform coefficient Histograms. Our results demonstrate that combing the spatial method of histogram equalization with logarithmic transform domain coefficient Histograms achieves a much more balanced enhancement, that out performs classical histogram equalization.

  • contrast entropy based image enhancement and logarithmic transform coefficient histogram shifting
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    This paper presents an enhancement technique based upon a new application of Histograms on transform domain coefficients called logarithmic transform coefficient histogram shifting (LTHS). A measure of enhancement based on contrast entropy is used as a tool for evaluating the performance of the proposed enhancement technique and for finding optimal values for variables contained in the enhancement. The algorithm's performance is compared quantitatively to classical histogram equalization using the aforementioned measure of enhancement. Experimental results are presented to show the performance of the proposed algorithm alongside classical histogram equalization.

Sos Agaian - One of the best experts on this subject based on the ideXlab platform.

  • wavelet transform coefficient histogram based image enhancement algorithms
    Proceedings of SPIE, 2010
    Co-Authors: Junjun Xia, Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the spatial method of histogram equalization with the logarithmic transform domain coefficient Histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • Image enhancement based on transform coefficient histogram shifting and shaping
    2010 IEEE International Conference on Technologies for Homeland Security (HST), 2010
    Co-Authors: Karen Panetta, Sos Agaian
    Abstract:

    This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the spatial method of histogram equalization with the wavelet transform domain coefficient Histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

  • logarithmic transform coefficient histogram matching with spatial equalization
    Visual Information Processing Conference, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    In this paper we propose an image enhancement algorithm that is based on utilizing histogram data gathered from transform domain coefficients that will improve on the limitations of the histogram equalization method. Traditionally, classical histogram equalization has had some problems due to its inherent dynamic range expansion. Many images with data tightly clustered around certain intensity values can be over enhanced by standard histogram equalization, leading to artifacts and overall tonal change of the image. In the transform domain, one has control over subtle image properties such as low and high frequency content with their respective magnitudes and phases. However, due to the nature of many of these transforms, the coefficient’s Histograms may be so tightly packed that distinguishing them from one another may be impossible. By placing the transform coefficients in the logarithmic transform domain, it is easy to see the difference between different quality levels of images based upon their logarithmic transform coefficient Histograms. Our results demonstrate that combing the spatial method of histogram equalization with logarithmic transform domain coefficient Histograms achieves a much more balanced enhancement, that out performs classical histogram equalization.

  • contrast entropy based image enhancement and logarithmic transform coefficient histogram shifting
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    This paper presents an enhancement technique based upon a new application of Histograms on transform domain coefficients called logarithmic transform coefficient histogram shifting (LTHS). A measure of enhancement based on contrast entropy is used as a tool for evaluating the performance of the proposed enhancement technique and for finding optimal values for variables contained in the enhancement. The algorithm's performance is compared quantitatively to classical histogram equalization using the aforementioned measure of enhancement. Experimental results are presented to show the performance of the proposed algorithm alongside classical histogram equalization.

Patrice Brault - One of the best experts on this subject based on the ideXlab platform.

  • Gaussian mixture model-based contrast enhancement
    IET Image Processing, 2015
    Co-Authors: Mohsen Abdoli, Hossein Sarikhani, Mohammad Ghanbari, Patrice Brault
    Abstract:

    In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of Histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual Histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis shows that GMMCE is a low-complexity method.

  • Gaussian Mixture Model Based Contrast Enhancement
    IET Image Processing, 2015
    Co-Authors: Mohsen Abdoli, Hossein Sarikhani, Mohammad Ghanbari, Patrice Brault
    Abstract:

    In this paper, a method for enhancing low contrast images is proposed. This method, called Gaussian Mixture Model based Contrast Enhancement (GMMCE), brings into play the Gaussian mixture modeling of Histograms to model the content of the images. Based on the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low contrast images into a set of scaled and shifted Gaussians. The individual Histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimized to set up a GMM with lowest approximation error and highest similarity to the original histogram. Compared to the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis show that GMMCE is a low complexity method.

B Silver - One of the best experts on this subject based on the ideXlab platform.

  • logarithmic transform coefficient histogram matching with spatial equalization
    Visual Information Processing Conference, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    In this paper we propose an image enhancement algorithm that is based on utilizing histogram data gathered from transform domain coefficients that will improve on the limitations of the histogram equalization method. Traditionally, classical histogram equalization has had some problems due to its inherent dynamic range expansion. Many images with data tightly clustered around certain intensity values can be over enhanced by standard histogram equalization, leading to artifacts and overall tonal change of the image. In the transform domain, one has control over subtle image properties such as low and high frequency content with their respective magnitudes and phases. However, due to the nature of many of these transforms, the coefficient’s Histograms may be so tightly packed that distinguishing them from one another may be impossible. By placing the transform coefficients in the logarithmic transform domain, it is easy to see the difference between different quality levels of images based upon their logarithmic transform coefficient Histograms. Our results demonstrate that combing the spatial method of histogram equalization with logarithmic transform domain coefficient Histograms achieves a much more balanced enhancement, that out performs classical histogram equalization.

  • contrast entropy based image enhancement and logarithmic transform coefficient histogram shifting
    International Conference on Acoustics Speech and Signal Processing, 2005
    Co-Authors: B Silver, Sos Agaian, Karen Panetta
    Abstract:

    This paper presents an enhancement technique based upon a new application of Histograms on transform domain coefficients called logarithmic transform coefficient histogram shifting (LTHS). A measure of enhancement based on contrast entropy is used as a tool for evaluating the performance of the proposed enhancement technique and for finding optimal values for variables contained in the enhancement. The algorithm's performance is compared quantitatively to classical histogram equalization using the aforementioned measure of enhancement. Experimental results are presented to show the performance of the proposed algorithm alongside classical histogram equalization.

Mohsen Abdoli - One of the best experts on this subject based on the ideXlab platform.

  • Gaussian mixture model-based contrast enhancement
    IET Image Processing, 2015
    Co-Authors: Mohsen Abdoli, Hossein Sarikhani, Mohammad Ghanbari, Patrice Brault
    Abstract:

    In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of Histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual Histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis shows that GMMCE is a low-complexity method.

  • Gaussian Mixture Model Based Contrast Enhancement
    IET Image Processing, 2015
    Co-Authors: Mohsen Abdoli, Hossein Sarikhani, Mohammad Ghanbari, Patrice Brault
    Abstract:

    In this paper, a method for enhancing low contrast images is proposed. This method, called Gaussian Mixture Model based Contrast Enhancement (GMMCE), brings into play the Gaussian mixture modeling of Histograms to model the content of the images. Based on the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low contrast images into a set of scaled and shifted Gaussians. The individual Histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimized to set up a GMM with lowest approximation error and highest similarity to the original histogram. Compared to the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis show that GMMCE is a low complexity method.