Abstract
In this work, we have presented a general framework for reconstruction of intensity images based on new sets of Generalized Fractional order of Chebyshev orthogonal Moments (GFCMs), a novel set of Fractional order orthogonal Laguerre Moments (FLMs) and Generalized Fractional order orthogonal Laguerre Moments (GFLMs). The fractional and generalized recurrence relations of fractional order Chebyshev functions are defined. The fractional and generalized fractional order Laguerre recurrence formulas are given. The new presented generalized fractional order moments are tested with the existing orthogonal moments classical Chebyshev moments, Laguerre moments, and Fractional order Chebyshev Moments (FCMs). The numerical results show that the importance of our general framework which gives a very comprehensive study on intensity image representation based GFCMs, FLMs, and GFLMs. In addition, the fractional parameters give a flexibility of studying global features of images at different positions and scales of the given moments.
Highlights
We focused on the problem of image reconstruction using a set of fractional order generalized orthogonal moments that allow us to use a set of parameters for each distribution separately and study the properties of each image
In order to examine the priority of the proposed novel generalized fractional order of Chebyshev orthogonal moments (GFCMs), Generalized Laguerre Orthogonal Moments (GLMs), and Generalized Fractional order orthogonal Laguerre Moments (GFLMs), we computed computed the computational performance of the proposed fractional order moments
We introduced a new general framework for representing images based on three new sets of generalized fractional order of Chebyshev orthogonal moments (GFCMs), generalized Laguerre
Summary
We focused on the problem of image reconstruction using a set of fractional order generalized orthogonal moments that allow us to use a set of parameters for each distribution separately and study the properties of each image. Image representation based on a set of fractional order orthogonal moments is presented by researchers. A set of fractional order orthogonal Chebyshev moments are used to represent gray-scale image [4]. We have introduced two generalized bivariate polynomials and we have constructed a stable and orthogonal moments GFCM and GFLMs. J.
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