Optimum signal and image recovery by the method of alternating projections in fractional Fourier domains

Abstract This paper presents a signal and image recovery scheme by the method of alternating projections onto convex sets in optimum fractional Fourier domains. It is shown that the fractional Fourier domain order with minimum bandwidth is the optimum fractional Fourier domain for the method employing alternating projections in signal recovery problems. Following the estimation of optimum fractional Fourier transform orders, incomplete signal is projected onto different convex sets consecutively to restore the missing part. Using a priori information in optimum fractional Fourier domains, superior results are obtained compared to the conventional Fourier domain restoration. The algorithm is tested on 1-D linear frequency modulated signals, real biological data and 2-D signals presenting chirp-type characteristics. Better results are obtained in the matched fractional Fourier domain, compared to not only the conventional Fourier domain restoration, but also other fractional Fourier domains.

[1]  Graham Kendall,et al.  Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques , 2013 .

[2]  Ahmet Enis Cetin,et al.  Resolution enhancement of low resolution wavefields with POCS algorithm , 2003 .

[3]  R. Gerchberg Super-resolution through Error Energy Reduction , 1974 .

[4]  Dirk Thierens,et al.  Elitist recombination: an integrated selection recombination GA , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[5]  M. Fatih Erden,et al.  Repeated filtering in consecutive fractional Fourier domains and its application to signal restoration , 1999, IEEE Trans. Signal Process..

[6]  Z. Zalevsky,et al.  The Fractional Fourier Transform: with Applications in Optics and Signal Processing , 2001 .

[7]  H. Ozaktas,et al.  Fractional Fourier transforms and their optical implementation. II , 1993 .

[8]  Paulo Jorge S. G. Ferreira Interpolation and the discrete Papoulis-Gerchberg algorithm , 1994, IEEE Trans. Signal Process..

[9]  Huifang Sun,et al.  Concealment of damaged block transform coded images using projections onto convex sets , 1995, IEEE Trans. Image Process..

[10]  K. K. Sharma,et al.  Extrapolation of signals using the method of alternating projections in fractional Fourier domains , 2008, Signal Image Video Process..

[11]  Kamalesh Kumar Sharma,et al.  Papoulis-like generalized sampling expansions in fractional Fourier domains and their application to superresolution , 2007 .

[12]  M. Sezan,et al.  Tomographic Image Reconstruction from Incomplete View Data by Convex Projections and Direct Fourier Inversion , 1984, IEEE Transactions on Medical Imaging.

[13]  Orhan Arikan,et al.  Short-time Fourier transform: two fundamental properties and an optimal implementation , 2003, IEEE Trans. Signal Process..

[14]  D. Youla,et al.  Image Restoration by the Method of Convex Projections: Part 1ߞTheory , 1982, IEEE Transactions on Medical Imaging.

[15]  P. Jansson Deconvolution of images and spectra , 1997 .

[16]  M. Sezan,et al.  Image Restoration by the Method of Convex Projections: Part 2-Applications and Numerical Results , 1982, IEEE Transactions on Medical Imaging.

[17]  A. Zayed A convolution and product theorem for the fractional Fourier transform , 1998, IEEE Signal Process. Lett..

[18]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[19]  A. Papoulis A new algorithm in spectral analysis and band-limited extrapolation. , 1975 .

[20]  Olcay Akay,et al.  Fractional convolution and correlation via operator methods and an application to detection of linear FM signals , 2001, IEEE Trans. Signal Process..

[21]  P. L. Combettes,et al.  Foundation of set theoretic estimation , 1993 .

[22]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[23]  Gunnar Karlsson,et al.  Packet video and its integration into the network architecture , 1989, IEEE J. Sel. Areas Commun..

[24]  C. Moss,et al.  Echolocation in bats and dolphins , 2003 .

[25]  R. Marks,et al.  Signal synthesis in the presence of an inconsistent set of constraints , 1985 .

[26]  Douglas H. Werner,et al.  Genetic Algorithms in Electromagnetics , 2007 .

[27]  Heinz H. Bauschke,et al.  On Projection Algorithms for Solving Convex Feasibility Problems , 1996, SIAM Rev..

[28]  Soo-Chang Pei,et al.  Two dimensional discrete fractional Fourier transform , 1998, Signal Process..

[29]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[30]  N. Bose,et al.  Two-dimensional discrete Hilbert transform and computational complexity aspects in its implementation , 1979 .

[31]  Xin Yao,et al.  Population-Based Incremental Learning With Associative Memory for Dynamic Environments , 2008, IEEE Transactions on Evolutionary Computation.

[32]  Dante C. Youla,et al.  Generalized Image Restoration by the Method of Alternating Orthogonal Projections , 1978 .

[33]  Karl J. Friston,et al.  Studying spontaneous EEG activity with fMRI , 2003, Brain Research Reviews.

[34]  Robert J. Marks,et al.  Recovery of image blocks using the method of alternating projections , 2005, IEEE Transactions on Image Processing.

[35]  P. L. Combettes,et al.  The Convex Feasibility Problem in Image Recovery , 1996 .

[36]  Ii Robert J. Marks,et al.  Alternating projections onto convex sets , 1996 .

[37]  Dirk Thierens,et al.  Selection Schemes, Elitist Recombination, and Selection Intensity , 1997, ICGA.

[38]  Billur Barshan,et al.  Signal recovery from partial fractional Fourier domain information and its applications , 2008 .

[39]  P. L. Combettes The foundations of set theoretic estimation , 1993 .

[40]  David Posada,et al.  Automated phylogenetic detection of recombination using a genetic algorithm. , 2006, Molecular biology and evolution.

[41]  Gozde Bozdagi Akar,et al.  Digital computation of the fractional Fourier transform , 1996, IEEE Trans. Signal Process..

[42]  D. Rimm,et al.  Classification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays , 2006, Clinical Cancer Research.