scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

Image fusion scheme using two dimensional discrete fractional Fourier transform

TL;DR: A novel fusion scheme based on 2D-discrete fractional Fourier transform (2D-DFRFT) that is better in terms of the fusion quality metrics as compared to the existing fusion schemes is proposed.
Abstract: In this paper, a novel fusion scheme based on 2D-discrete fractional Fourier transform (2D-DFRFT) is proposed. In the proposed scheme, input source images are transformed using 2D-DFRFT and then subtracted from the respective source images to obtained the detail images. The detail images are further used to generate the fused image using appropriate fusion rule. The additional degree of freedom in terms of its angle parameters associated with the 2D-DFRFT is exploited for obtaining better results in the proposed fusion scheme. Performance evaluation of fused images is done by computing fusion quality metrics and the fusion results are compared with other existing fusion schemes. It is seen that the performance of the proposed scheme is better in terms of the fusion quality metrics as compared to the existing fusion schemes.
Citations
More filters
Journal ArticleDOI
TL;DR: In this article, an ensemble technique of image segmentation is implemented to segment the tumor region of the brain MRI image. And the average accuracy for the proposed work is Â98% in Ensemble 1 and Â97% in ensemble 2 methods for the BraTS brain image dataset.
Abstract: The Accurate segmentation and classification takes place a major role in the medical image processing to detect and locate the abnormal tissue region. In this, the three different types of brain magnetic resonance imaging (MRI) image source such as Type-1, Type-2 and Fluid attenuated inversion recovery are combined by the image registration process to detect the clear region of the tumor tissue since, the region of interest identification in the single image data contains less key points to define it. In this paper, we implement the ensemble technique of image segmentation to segment the tumor region of the brain MRI image. For the segmentation process, the images are pre-processed by Laplacian cellular automata filtering method and segmented by ensemble of different clustering method such as K-means, fuzzy based clustering, self-organization map (SOM) and ensemble of Gaussian mixture model, K-means, SOM and their results are compared. This ensemble cluster label is consider as the segmented result and classify the abnormalities by using deep super learning method. The experimental results and the comparison charts defines the performance rate of proposed method comparing to the other state-of-art methods. The average accuracy for the proposed work is 98% in Ensemble 1 and 97% in Ensemble 2 methods for the BraTS brain image dataset.

5 citations

References
More filters
Journal ArticleDOI
TL;DR: In this article, an image fusion scheme based on the wavelet transform is presented, where wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transformation of the fused wavelet coefficients.

1,532 citations

Journal ArticleDOI
TL;DR: Experimental results clearly indicate that this metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms.
Abstract: A measure for objectively assessing the pixel level fusion performance is defined. The proposed metric reflects the quality of visual information obtained from the fusion of input images and can be used to compare the performance of different image fusion algorithms. Experimental results clearly indicate that this metric is perceptually meaningful.

1,446 citations


"Image fusion scheme using two dimen..." refers methods in this paper

  • ...The Q , metric measures the amount of edge information from source images to fused image is given by [26] Q = ∑M m=1 ∑N n=1(Q AF (m,n)w(m,n) + (Q (m,n)w(m,n)) ∑M m=1 ∑N n=1(w A(m,n) + wB(m,n)) ,...

    [...]

  • ...In this section, performance parameters [20], [21], [26] are used to compare the effectiveness of proposed fusion scheme which are discussed below....

    [...]

Journal ArticleDOI
TL;DR: In this paper, the linear transform kernel for fractional Fourier transform is derived and the spatial resolution and the space-bandwidth product for propagation in graded-index media are discussed.
Abstract: The linear transform kernel for fractional Fourier transforms is derived. The spatial resolution and the space–bandwidth product for propagation in graded-index media are discussed in direct relation to fractional Fourier transforms, and numerical examples are presented. It is shown how fractional Fourier transforms can be made the basis of generalized spatial filtering systems: Several filters are interleaved between several fractional transform stages, thereby increasing the number of degrees of freedom available in filter synthesis.

806 citations

Journal ArticleDOI
TL;DR: This definition is based on a particular set of eigenvectors of the DFT matrix, which constitutes the discrete counterpart of the set of Hermite-Gaussian functions, and is exactly unitary, index additive, and reduces to the D FT for unit order.
Abstract: We propose and consolidate a definition of the discrete fractional Fourier transform that generalizes the discrete Fourier transform (DFT) in the same sense that the continuous fractional Fourier transform generalizes the continuous ordinary Fourier transform. This definition is based on a particular set of eigenvectors of the DFT matrix, which constitutes the discrete counterpart of the set of Hermite-Gaussian functions. The definition is exactly unitary, index additive, and reduces to the DFT for unit order. The fact that this definition satisfies all the desirable properties expected of the discrete fractional Fourier transform supports our confidence that it will be accepted as the definitive definition of this transform.

604 citations

Book
23 Jul 1993
TL;DR: The Feichtinger-Grochenig sampling theory as mentioned in this paper is a generalization of Shannon sampling Theorem and band-limited sampling theory, which is used for multidimensional signals.
Abstract: Introduction and a Historical Overview. Shannon Sampling Theorem and Band-Limited Signals. Generalizations of Shannon Sampling Theorems. Sampling Theorems Associated with Sturm-Liouville Boundary-Value Problems. Sampling Theorems Associated with Self-Adjoint Boundary-Value Problems. Sampling by Using Green's Function. Sampling Theorems and Special Functions. Kramer's Sampling Theorem and Lagrange-Type Interpolation in N Dimensions. Sampling Theorems for Multidimensional Signals-The Feichtinger-Grochenig Sampling Theory. Frames and Wavelets: A New Perspective on Sampling Theorems.

516 citations