The Experts below are selected from a list of 14751 Experts worldwide ranked by ideXlab platform

Steven Simske - One of the best experts on this subject based on the ideXlab platform.

  • Atmospheric Turbulence Degraded-Image Restoration by Kurtosis Minimization
    IEEE Geoscience and Remote Sensing Letters, 2009
    Co-Authors: Dalong Li, Steven Simske
    Abstract:

    Atmospheric turbulence is caused by the random fluctuations of the refraction index of the medium. It can lead to blurring in Images acquired from a long distance away. Since the degradation is often not completely known, the problem is viewed as blind Image deconvolution or blur identification. Our previous work has observed that blurring increases kurtosis and introduced a new blur identification method based on kurtosis minimization (KM). In this letter, this observation has been studied using phase correlation. The KM method is compared with two other signal processing methods. The limitation of the method is also discussed.

  • Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis
    IEEE Geoscience and Remote Sensing Letters, 2007
    Co-Authors: Dalong Li, Russell M. Mersereau, Steven Simske
    Abstract:

    Our earlier work revealed a connection between blind Image deconvolution and principal components analysis (PCA). In this letter, we explicitly formulate multichannel and single-channel blind Image deconvolution as a PCA problem. Although PCA is derived from blur models that do not contain additive noise, it can be justified on both theoretical and experimental grounds that the PCA-based restoration algorithm is actually robust to the presence of white noise. The algorithm is applied to the restoration of atmospheric turbulence-Degraded Imagery and compared to an adaptive Lucy-Richardson maximum-likelihood algorithm on both real and simulated atmospheric turbulence blurred Images. It is shown that the PCA-based blind Image deconvolution runs faster and is more robust to noise.

Peng Dong-liang - One of the best experts on this subject based on the ideXlab platform.

  • SMC - Degraded Image enhancement with applications in robot vision
    2005 IEEE International Conference on Systems Man and Cybernetics, 1
    Co-Authors: Peng Dong-liang
    Abstract:

    The theory of fuzzy sets has been used to deal with Image enhancement problems for Degraded Images in which the Image edges are uncertain and inaccurate. For those kinds of Images, to some extent, the good enhancement effect can be obtained using the fuzzy sets-based Image enhancement method instead of the traditional Image enhancement approaches. The gray level maximum has not been changed in the classical fuzzy enhancement method proposed by S. K. Pal, so this method is not fit for the enhancement problem of Degraded Images with less gray levels and low contrasts; the fact that the range of membership function of gray levels is not normalization form, i.e. [0,1], is another disadvantage of the traditional fuzzy enhancement approach. To deal with the problems mentioned above, a generalized iterative fuzzy enhancement algorithm is proposed in this paper. A new Image quality assessment criterion is suggested on the basis of the statistical features of the gray-level histogram of Images to control the iterative procedure of the proposed Image enhancement algorithm. Computer simulation results showed that this new enhancement method is more suitable than fuzzy enhancement and gray-level transformation for handling the enhancement problems of Images with less gray levels and low contrasts.

Xue Anke - One of the best experts on this subject based on the ideXlab platform.

  • Degraded Image enhancement with applications in robot vision
    Systems Man and Cybernetics, 2005
    Co-Authors: Peng Dongliang, Xue Anke
    Abstract:

    The theory of fuzzy sets has been used to deal with Image enhancement problems for Degraded Images in which the Image edges are uncertain and inaccurate. For those kinds of Images, to some extent, the good enhancement effect can be obtained using the fuzzy sets-based Image enhancement method instead of the traditional Image enhancement approaches. The gray level maximum has not been changed in the classical fuzzy enhancement method proposed by S. K. Pal, so this method is not fit for the enhancement problem of Degraded Images with less gray levels and low contrasts; the fact that the range of membership function of gray levels is not normalization form, i.e. [0,1], is another disadvantage of the traditional fuzzy enhancement approach. To deal with the problems mentioned above, a generalized iterative fuzzy enhancement algorithm is proposed in this paper. A new Image quality assessment criterion is suggested on the basis of the statistical features of the gray-level histogram of Images to control the iterative procedure of the proposed Image enhancement algorithm. Computer simulation results showed that this new enhancement method is more suitable than fuzzy enhancement and gray-level transformation for handling the enhancement problems of Images with less gray levels and low contrasts.

Dalong Li - One of the best experts on this subject based on the ideXlab platform.

  • Atmospheric Turbulence Degraded-Image Restoration by Kurtosis Minimization
    IEEE Geoscience and Remote Sensing Letters, 2009
    Co-Authors: Dalong Li, Steven Simske
    Abstract:

    Atmospheric turbulence is caused by the random fluctuations of the refraction index of the medium. It can lead to blurring in Images acquired from a long distance away. Since the degradation is often not completely known, the problem is viewed as blind Image deconvolution or blur identification. Our previous work has observed that blurring increases kurtosis and introduced a new blur identification method based on kurtosis minimization (KM). In this letter, this observation has been studied using phase correlation. The KM method is compared with two other signal processing methods. The limitation of the method is also discussed.

  • Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis
    IEEE Geoscience and Remote Sensing Letters, 2007
    Co-Authors: Dalong Li, Russell M. Mersereau, Steven Simske
    Abstract:

    Our earlier work revealed a connection between blind Image deconvolution and principal components analysis (PCA). In this letter, we explicitly formulate multichannel and single-channel blind Image deconvolution as a PCA problem. Although PCA is derived from blur models that do not contain additive noise, it can be justified on both theoretical and experimental grounds that the PCA-based restoration algorithm is actually robust to the presence of white noise. The algorithm is applied to the restoration of atmospheric turbulence-Degraded Imagery and compared to an adaptive Lucy-Richardson maximum-likelihood algorithm on both real and simulated atmospheric turbulence blurred Images. It is shown that the PCA-based blind Image deconvolution runs faster and is more robust to noise.

Hong Han-yu - One of the best experts on this subject based on the ideXlab platform.

  • Fast restoration for turbulence-Degraded Images based on second-order weighted difference
    Computer Engineering and Applications, 2008
    Co-Authors: Hong Han-yu
    Abstract:

    For the requirement of fast restoration of the turbulence-Degraded Image with complex background in space flight craft imaging system,the theory of 2-D convolution,and construct a shift operator are studied.A fast restoration algorithm for the two neighboring frames of turbulence-Degraded Images based on the theory of 2-D convolution and shift operator is proposed,which incorporating the minimization of the second-order weighted differences in the process of the restoration for the turbulence-Degraded Images.Some second-order weighted difference items are defined,and a fast method to construct the matrix of the weighted second-order operator is derived.The values of the point spread function can be solved by converting their non-linear estimation into fast solution of linear equations based on lagged iterative technique,and it provides a quickly and effect method for the restoration for the turbulence-Degraded Image with complex background.The experiment results show that,the proposed algorithm is effective,stable and fast for the restoration of turbulence-Degraded Image in real situation.

  • Optimization restoration algorithm for infrared object turbulence-Degraded Image
    Journal of Applied Optics, 2006
    Co-Authors: Hong Han-yu, Yu Jiu-yang
    Abstract:

    A blind restoration algorithm for turbulence-Degraded Image based on the steepest descent method is proposed.An objective function based on frequency spectrums of the object Image and the point-spread function(PSF) is set up in the frequency domain,which is minimized by the steepest descent method in an iterative manner.FFT and IFFT are used to transfer the object Image and the PSF between frequency domain and time domain,and the constraints of the frequency domain and space domain are introduced in each iteration to modify them,so as to obtain the expected Image restoration effect,the proposed algorithm robustness and better immunity to noise.A series of restoration experiments for infrared object turbulence-Degraded Images are performed to test the proposed algorithm in the microcomputer,and the experimental results show that the proposed algorithm is robust and immune to noise.

  • Investigation on Restoration Method for Turbulence-Degraded Image Using Bayes Theorem
    Journal of Image and Graphics, 2005
    Co-Authors: Yu Guo-liang, Zhang Tian-xu, Hong Han-yu, Wang Ning-yu
    Abstract:

    Restoration of atmospheric turbulence-Degraded Image is very important in the field of astronomical imaging and astroobservation.It needs to be solved as soon as possible.Solving this problem can deblur the atmospheric turbulence-Degraded Image and improve the capability of object identification which is good for late stage works such as object feature extraction and recognition.In order to restore the turbulence-Degraded Image efficiently,a single frame blind deconvolution Image restoration algorithm with two circulations based on Bayes theorem is presented.Its fast implementation is studied and some experiments to analyze the stability are carried out.The experimental results show that the algorithm is capable of resisting-noise with robustness;especially it is more suitable for the situation without any prior knowledge.So the algorithm possesses practical value.