Parallel algorithms for a hypercomplex discrete fourier transform

Methods for the parallel computation of a multidimensional hypercomplex discrete Fourier transform (HDFT) are considered. The basic idea consists in the application of the properties of the hypercomplex algebra in which this transform is performed. Additional possibilities for increasing the efficiency of the algorithm are provided by the natural parallelism of the multidimensional Cooley-Tukey scheme. Marat Vyacheslavovich Aliev. Born 1978. Graduated from the Adygeya State University in 2000. Received candidate’s degree in physics and mathematics in 2004. Presently he is a senior lecturer at the Department of Applied Mathematics and Information Technologies, Adygeya State University. Scientific interests: image processing, fractals, fast algorithms of discrete transforms, and finite-dimensional algebras. Author of 14 publications, including 7 papers. Member of the Russian Association of Pattern Recognition and Image Analysis. Aleksandr Mikhailovich Belov. Born 1980. Graduated from the Samara State Aerospace University in 2002. In the same year, he entered postgraduate courses with the specialty 05.13.18: mathematical modeling, numerical methods, and program complexes. Presently he is a postgraduate student at the Department of Geoinformatics, Samara State Aerospace University, and a trainee at the Laboratory of Mathematical Methods of Image Processing, Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: discrete orthogonal transforms, fast algorithms of discrete orthogonal transforms, and theory of canonical systems of calculus. Author of 13 publications, including 5 papers. Member of the Russian Association of Pattern Recognition and Image Analysis. Aleksei Vladimirovich Ershov. Born 1983. In 2000, he graduated from the Samara Lyceum of Economics and entered the Faculty of Mechanics and Mathematics, Samara State University, to specialize in the field of Organization and Technology of Information Security. In 2001, he started his training within an additional educational program and was qualified as a translator in the field of professional communication. Presently he is a fifth-year student at Samara State University. The title of his diploma work is “Control of the Flows of Confidential Information.” He is an active participant in the translation of the monograph Principia Mathematica, Cambridge University Press, 1927, by A. Whitehead and B. Russell. Author of four publications, including two papers. Marina Aleksandrovna Chicheva. Born 1964. Graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1987. Received candidate’s degree in Engineering in 1998. Presently she is a senior researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: image processing, compression, and fast algorithms of discrete transforms. Author of more than 50 publications, including 18 papers and 1 monograph. Member of the Russian Association of Pattern Recognition and Image Analysis.     

Efficiency of the reduced wise algorithm over a set of convolution algorithms

A reduction operation and the design of a reduced wise algorithm over a set of known algorithms of unvarying complexity are addressed. A direct convolution algorithm and the best-known algorithms based on fast discrete orthogonal transforms (with Cooley-Tukey and Good-Thomas decompositions and the Rader algorithm for short lengths) are used as a support set of known algorithms. It is shown that their combined use in the reduced wise algorithm decreases the computational complexity of the resulting convolution algorithm as compared to that of the algorithms in the support set. Alina Yur’evna Bavrina. Born 1980. Graduated from the Samara State Aerospace University in 2003. Received candidate’s degree in technical sciences in 2006. Junior researcher at the Image Processing Systems Institute of the Russian Academy of Sciences. Research interests: image processing, image compression, and geoinformation technology. Author of more than 20 publications, including 6 papers. Member of the Russian Association for Pattern Recognition and Image Analysis. Vladislav Valer’evich Myasnikov. Born 1971. Graduated from the Samara State Aerospace University (SSAU) in 1994. Started his graduate study at SSAU in 1995 and received candidate’s degree in technical sciences in 1998. Senior researcher at the Image Processing Systems Institute of the Russian Academy of Sciences and associate professor at the SSAU Department of Geoinformatics. Research interests: digital signal and image processing, geoinformatics, neural networks, and pattern recognition. Author of more than 60 publications, including 24 papers and 1 monograph (coauthored). Member of the Russian Association for Pattern Recognition and Image Analysis.     

Fast algorithms for a multidimensional hypercomplex discrete Fourier transform with radix-3 decomposition

Combined algorithms for the multidimensional hypercomplex discrete Fourier transform (HDFT) of a real signal with data representation in the Hamilton-Eisenstein generalized codes are synthesized. The complexity of arithmetic operations in a commutative-associative hypercomplex algebra and its representation in generalized codes are obtained. It is shown that there exist only two essentially different commutative-associative hypercomplex algebras: the direct sums of real or complex algebras. The computational complexity of the algorithm synthesized is estimated. Marat Vyacheslavovich Aliev. Born 1978. Graduated from Adygeya State University in 2000. Received candidate’s degree in physics and mathematics in 2004. Presently he is a senior lecturer at the Department of Applied Mathematics and Information Technologies, Adygeya State University. Scientific interests: image processing, fractals, fast algorithms of discrete transforms, and finite-dimensional algebras. Author of 14 publications, including 7 papers. Member of the Russian Association of Pattern Recognition and Image Analysis. Marina Aleksandrovna Chicheva. Born 1964. Graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1987. Received candidate’s degree in Engineering in 1998. Presently, she is a senior researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: image processing, compression, and fast algorithms of discrete transforms. Author of more than 50 publications, including 18 papers and 1 monograph. Member of the Russian Association of Pattern Recognition and Image Analysis.     

Efficient algorithms of local discrete wavelet transform with Haar-like bases

In this paper, two new fast algorithms for calculating a local discrete wavelet transform for a one-dimensional signal based on the example of Haar’s wavelet basis are presented and their computational complexities are evaluated. The algorithms are compared with each other and with well-known algorithms of fast wavelet transform. Employment recommendations are given for each algorithm presented. Among other things the preference domains of these algorithms are shown, i.e., the parameters of wavelet transform calculating problem are specified so that these algorithms are computationally efficient. Conclusions regarding the advantages of the recursive algorithm over the alternative one and over the well-known algorithm of the fast wavelet transform are reached using the analysis of algorithmic complexities as the base and with regard to additional possibilities of the recursive algorithm. The extension of the algorithms considered to a two-dimensional case is presented. Vasilii N. Kopenkov. Was born in 1978. He was graduated in Samara State Aerospace University in 2001. At present time he works as an assistant on chair of geoinformatics in SSAU and as a junior research assistant in Institute of Image Processing Systems RAS. Head interests: image processing, earth remote sensing data and pattern recognition, geoinformatic systems. Author of 17 publications, including 7 articles. Member of the Russian Association Pattern Recognition and Image Analysis.     

Image restoration with sliding sinusoidal transforms

A space-variant restoration technique with sliding sinusoidal transforms is presented. Minimum mean-square error estimators in the domains of sliding discrete sine and cosine transforms for image restoration are derived. To achieve image processing in real time, fast recursive algorithms for computing the sliding sinusoidal transforms are utilized. Computer simulation results using a real image are provided and compared with that of common restoration techniques. The text was submitted by the authors in English. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, his PhD degree in 1992, and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at CICESE, México. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received Candidate’s degree in 1953 and Doctor’s degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Fast correlation method with the use of a wide selection of template in problems of image analysis

The problem of searching for and recognizing fragments of images that correspond to one of a wide variety of template is considered. The method of the fast correlation of a wide selection of trinary template, which successfully resolves this problem, is suggested. The use of this method in two problems of image analysis is shown, namely, the search for position of eyes in documental photographs of faces and the recognition of computer-readable lines in scanned images of documents. Nikolai Ivanovich Glumov. Born in 1962. In 1985, he graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1994, he defended the Candidate of Science (Engineering) Dissertation. Currently, he is working as the Senior Researcher at the Image Processing Systems Institute, Russian Academy of Sciences. His circle of scientific interests involves the image processing and pattern recognition, image compression, and simulation of the systems of formation of digital images. Glumov has more than 90 publications involving more than 30 articles and one monograph (in partnership). He is a member of the Russian Association of Pattern Recognition and Image Processing. Evgenii Valer’evich Myasnikov. Born in 1981. In 2004, he graduated the Samara State Aerospace University and entered the Post-Graduate Education of SGAU. In 2007, Myasnikov defended the Candidate of Science (Engineering) Dissertation. Currently, is working as the Probationer Researcher at the Image Processing Systems Institute, Russian Academy of Sciences and simultaneously as the Assistant of the Department of Geoinformatics at SCAU. The circle of scientific interests involves the creation of software complexes, image processing and pattern recognition, and search for images in databases. Myasnikov has 23 publications, including six articles. He is the member of the Russian Association of Pattern Recognition and Image Processing. Vasilii Nikolaevich Kopenkov. Born in 1978. In 2001, he graduated the Samara State Aerospace University (SGAU). Currently, he is working as the assistant of the Department of Geoinformatics at the SGAU and Junior Researcher at the Image Processing Systems Institute, Russian Academy of Sciences. The circle of scientific interests involves the processing of images of the distanced probing of the Earth, pattern recognition, and geoinformatic systems. Kopenkov has 17 publications, including seven articles. He is the member of the Russian Association of Pattern Recognition and Image Processing. Marina Aleksandrovna Chicheva. Born in 1964. In 1987, she graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1998, she defended the Candidate of Science (Engineering) Dissertation. She currently works as the Senior Researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Her scientific interests include image processing and compression, rapid algorithms of discrete transformations, and pattern recognition. Chicheva has more than 18 articles, including one monograph (in partnership). She is the member of the Russian Association of Pattern Recognition and Image Processing.     

Software environment for simulating algorithms for image analysis and processing

We consider the purpose, functionality, configuration, and structure of a software environment designed for simulation and investigation of methods, algorithms, and information technology for digital images analysis and processing. Mikhail V. Gashnikov. Born 1975. Graduated from the Samara State Airspace University (SSAU) in 1998. Received candidate’s degree in Technology in 2004. He is now an assistant professor at the chair of earth information of the SSAU. Scientific interests: image processing, compression, statistical coding. Author of more than 30 publications, including 12 papers and one monograph (in coauthorship). Member of the Russian Association for Pattern Recognition and Image Analysis. Evgenii V. Myasnikov. Born 1981. Graduated from the Samara State Airspace University in 2004. He is now a post-graduate student at the Chair of Earth Information of the Samara State Airspace University. Scientific interests: development of software systems, image processing, image retrieval in databases. Author of 6 publications, including one paper. Member of the Russian Association for Pattern Recognition and Image Analysis. Andrei V. Chernov. Born 1975. Graduated from the Samara State Airspace University (SSAU) in 1998. Received candidate’s degree in Technology in 2004. He is now an assistant professor at the Chair of Earth Information of the SSAU and a research fellow at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image processing, pattern recognition, geoinformation systems. Author of more than 50 publications, including 11 papers and one monograph (in coauthorship). Member of the Russian Association for Pattern Recognition and Image Analysis. Nikolai I. Glumov. Born 1962. Graduated from the Kuibyshev Airspace Institute (at present, the Samara State Airspace University) in 1985. Received candidate’s degree in Technology in 1994. He is now a senior researcher at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image processing and pattern recognition, compression of images, simulation of systems of digital image formation. Author of more than 50 publications, including 21 papers and one monographs (in coauthorsip). Member of the Russian Association for Pattern Recognition and Image Analysis. Vladislav V. Sergeev. Born 1951. Graduated from the Kuibyshev Airspace Institute (at present, the Samara State Airspace University) in 1974. Received doctoral degree in Technology in 1993. Head of the Laboratory of Mathematical Methods for Image Processing at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: digital signal processing, image analysis, pattern recognition, earth information. Author of more than 150 publications, including approximately 40 papers and two monographs (in coauthorship). President of the Povolzh’e Branch of the Russian Association for Pattern Recognition and Image Analysis. Corresponding member of the Russian Ecological Academy and of the Russian Academy of Engineering Sciences. Member of the International Society for Optical Engineering. A laureate of the Samara Provincial Government prize in science and engineering. Marina A. Chicheva. Born 1964. Graduated from the Kuibyshev Airspace Institute (at present, the Samara State Airspace University) in 1987. Received candidate’s degree in Technology in 1998. She is now a senior researcher at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image recognition, compression, fast algorithms for discrete transformations. Author of more than 40 publications, including 15 papers and one monograph (in coauthorship). Member of the Russian Association for Pattern Recognition and Image Analysis.     

Estimating directions of optic disk blood vessels in retinal images

The problem of determining directions of blood vessels in the optic disk is considered. The proposed method for estimating the vessel directions is based on analyzing local minima of gray-scale profile of the eye-ground image. Results of tests on real images are presented. Mikhail Anan’in. Born 1984. Graduated from the Samara State Aerospace University in 2007 and is currently a post-graduate student at the same university. From 2006 to present is a junior researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: image processing, image reconstruction, pattern recognition, wavelet analysis, and differential geometry. Authored more than ten papers. Nataliya Il’yasova. Born 1966. Graduated from the Samara State Aerospace University in 1991, where in 1997 she received candidate’s degree (Eng.). Currently a senior s researcher at the Image Processing Systems Institute, Russian Academy of Sciences and a senior lecturer at Samara State Aerospace University. Scientific interests: digital image processing and recognition, pattern recognition, information systems in biomedical applications, computer-aided systems for monitoring eye fundus microvascular morphology, and analysis of cardiac coronary vessels. Author of more than 60 papers in the field of image processing and pattern recognition. Aleksandr Kupriyanov. Born 1978. Graduated from the Samara State Aerospace University in 1991 and in 1997 received candidate’s degree (Eng.) from the same university. Currently has a position of researcher at the Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: digital image processing and recognition, pattern recognition, information systems in biomedical applications, computer-aided systems for monitoring eye fundus microvascular morphology, analysis of cardiac coronary vessels, evaluation of diagnostic features, and retinal image analysis. Author of more than 30 papers in the field of image processing and pattern recognition.     

Comparative analysis of function approximation methods applied to image processing

Application of nonlinear methods of multivariate regression approximation (neural networks, functions linear in fitting parameters, and hierarchical approximation) is considered to problems of image filtering based on a priori information in the form of matched pairs of images (“ideal” and “degraded”). The methods are compared with regard to their efficiency. Vasilii N. Kopenkov. Born 1978. Graduated from the Samara State Aerospace University (SSAU) in 2001. Assistant Professor at the Chair of Geoinformatics, SSAU, and a Junior Researcher at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image processing and pattern recognition. Author of four papers. Member of the Russian Federation Association for Pattern Recognition and Image Analysis. Andrei V. Chernov. Born 1975. Graduated from the Samara State Aerospace University (SSAU) in 1998. Received candidate’s degree (Cand. Sc. (Eng.)) in 2004. Assistant Professor at the Chair of Geoinformatics, SSAU, and a Researcher at the Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: image processing, pattern recognition, and geoinformation systems. Author of more than 50 publications, including 11 papers in journals, and a co-author of a monograph. Member of the Russian Federation Association for Pattern Recognition and Image Analysis. Vladislav V. Sergeev. Born 1951. Graduated from the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). Received doctoral degree (Dr. Sc. (Eng.)) in 1993. Head of Laboratory of Mathematical Methods of Image Processing, Institute of Image Processing Systems, Russian Academy of Sciences. Scientific interests: digital signal processing, image analysis, pattern recognition, and geoinformatics. Author of more than 150 publications, including about 40 papers in journals, and a co-author of 2 monographs. Chair of the Volga-region Branch of the Russian Federation Association for Pattern Recognition and Image Analysis. Corresponding Member of the Russian Ecological Academy and the Russian Academy of Engineering, member of SPIE (The International Society for Optical Engineering), a winner of the Samara District Award for Science and Engineering.     

Efficiency analysis of image compression algorithms based on hierarchical and wavelet representations

The efficiency of hierarchical and wavelet image compression methods is analyzed and compared. More specifically, hierarchical grid interpolation (HGI) is compared with JPEG-2000. The characteristics of both methods are analyzed, and recommendations are given concerning their use in various image-processing applications. Alina Yur’evna Bavrina. Born 1980. Graduated from the Samara State Aerospace University in 2003. Received her candidate’s degree in technical sciences in 2006. Junior researcher at the Image Processing Systems Institute of the Russian Academy of Sciences. Research interests: image processing, image compression, and geoinformation technology. Author of more than 20 publications, including 6 papers. Member of the Russian Association for Pattern Recognition and Image Analysis. Mikhail Valer’evich Gashnikov. Born 1975. Graduated from the Samara State Aerospace University (SSAU) in 1998. Received his candidate’s degree in technical sciences in 2002. Associate professor at the SSAU Department of Geoinformatics. Research interests: image processing, compression, and statistical coding. Author of more than 50 publications, including 21 papers and 1 monograph (coauthored). Member of the Russian Association for Pattern Recognition and Image Analysis. Nikolai Ivanovich Glumov. Born 1962. Graduated from the Kuibyshev Aviation Institute (now the Samara State Aerospace University) in 1985. Received candidate’s degree in technical sciences in 1994. Senior researcher at the Image Processing Systems Institute of the Russian Academy of Sciences. Research interests: image processing, pattern recognition, image compression, and simulation of digital image formation systems. Author of more than 90 publications, including more than 30 papers and 1 monograph (coauthored). Member of the Russian Association for Pattern Recognition and Image Analysis.     

An approximation-based approach to computing image moment invariants

A fast method for computing Hu’s image moment invariants is described. The invariants are found by approximation using generalized moments computed in a sliding window by a parallel recursive algorithm. The proposed method is shown to be computationally more efficient than direct computation. Vladislav V. Sergeev. Born 1951. Graduated from the Kuibyshev Aviation Institute (now, the Samara State Aerospace University) in 1974. Received doctoral degree (Dr. Sc. (Eng.)) in 1993. Head of Laboratory of Mathematical Methods of Image Processing, Image Processing Systems Institute, Russian Academy of Sciences. Scientific interests: digital signal processing, image analysis, pattern recognition, and geoinformatics. Author of more than 150 publications, including about 40 papers in journals, and a co-author of 2 monographs. Chair of the Volga-region Branch of the Russian Federation Association for Pattern Recognition and Image Analysis. Corresponding Member of the Russian Ecological Academy and the Russian Academy of Engineering, member of SPIE (The International Society for Optical Engineering), a winner of the Samara District Award for Science and Engineering. Ol’ga A. Titova. Born 1980. Graduated from the Samara State Aerospace University (SSAU) in 2002. Currently post-graduate student at the Chair of Geoinformatics, SSAU. Scientific interests: image analysis, pattern recognition, fast algorithms of digital image processing, and geoinformatics. Author of nine publications including three papers in journals. Member of the Russian Federation Association for Pattern Recognition and Image Analysis.     

Fast running algorithm for discrete <Emphasis Type="Italic">W</Emphasis> transform

A fast algorithm for computing the running type-II discrete W transform (DWT-II) is proposed. The algorithm is based on a recursive relationship between three subsequent local DWT-II spectra. The computational complexity of the algorithm is compared with that of known fast and running DWT-II algorithms. Fast inverse algorithms for signal processing in the domain of the running DWT-II are also proposed. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at the Centro de Investigacion Cientifica y de Educacion Superior de Ensenada (Cicese), Mexico. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseevich graduated from the Moscow Electrotechnical Institute of Telecommunications. Received candidate’s degree in 1953 and doctoral degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Complex approach to choosing algorithms of compression and error-correcting coding in digital image transmission via communication channels

The problem of choosing the algorithms of compression and error-correcting coding for transmission of digital images via communication channels is considered. The quality criteria for output images are analyzed and a technique for simulating errors (faults) in a communication channel is proposed. The possibility of considerable improvement of noise immunity of compressed images is demonstrated for the compression method based on hierarchical grid interpolation. Glumov Nikolai Ivanovich (b. 1962) graduated from Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1985. In 1994, he defended his Ph.D. (engineering) thesis. At present, Glumov is a senior scientist at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include image processing and recognition, image compression, and simulation of digital image formation systems. He has more than 60 publications, including 20 articles and a monograph (with co-authors). N.I. Glumov is a member of the Russian Association of Image Recognition and Analysis.     

Program and research system for the recognition of individuals by photos of faces on documents

In this article, the developed program and research system for recognizing individuals based on photos of faces on documents is described. The requirements for the system made at the development stage are determined. The most important of these requirements is the possibility of carrying out the investigations of various algorithms with the purpose of the comparing their efficiency, determining their optimum parameters, and selecting the best system of signs. The composition and destination of the main components of the program and research system for recognizing individuals are presented. The main feature of implementation is the use of processing scenarios in the system performed by an interpreter and the presence of an expanded set of the elemental processing functions. This solution results in the rapid development and variation of various algorithms of image processing, formation of signs, and classification. Evgenii Valer’evich Myasnikov. Born in 1981. In 2004, he graduated the Samara State Aerospace University (SGAU) and entered the Post-Graduate Education of SGAU. In 2007, Myasnikov defended the Candidate of Science (Engineering) Dissertation. Currently, he works as the Probationer Researcher at the Image Processing Systems Institute, Russian Academy of Sciences and simultaneously as the Assistant of the Department of Geoinformatics at SCAU. The circle of scientific interests involves the creation of software complexes, image processing, and pattern recognition, and search for images in databases. Myasnikov has 23 publications, including six articles. He is the member of the Russian Association of Pattern Recognition and Image Processing. Vladislav Viktorovich Sergeev. Born in 1951. In 1974, he graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1994, he defended his Doctor of Science (Engineering) dissertation. Currently, he works at the Chief of Laboratory of Mathematical Methods of Image Processing at the Image Processing Systems Institute, Russian Academy of Sciences. The circle of scientific interests involves the digital processing of signals, analysis of images, pattern recognition, and geoinformatics. Sergeev has more than 200 publications, including about 40 articles and two monographs (in partnership). Sergeev is the Chairman of the Povolzh’e Division of the Russian Association of Pattern Recognition and Image Analysis. He is the Corresponding Member of the Russian Environmental Academy and Academy of the Engineering Sciences of the Russian Federation, the member of SPIE (the International Society for Optical Engineering), and the Laureate of the Samara Regional Prize in the Field of Science and Engineering. Nikolai Ivanovich Glumov. Born in 1962. In 1985, he graduated the Kuibyshev Aviation Institute (now, the Samara State Aerospace University). In 1994, he defended the Candidate of Science (Engineering) Dissertation. Currently, he is working as the Senior Researcher at the Image Processing Institute, Russian Academy of Sciences. The circle of scientific interests involves the image processing and pattern recognition, image compression, and simulation of the systems of formation of digital images. Glumov has more than 90 publications, including more than 30 articles and one monograph (in partnership). He is the member of the Russian Association of Pattern Recognition and Image Processing. Aleksandr Pavlovich Chikhonadskikh. Born in 1959. In 1981, he graduated the Mozhaiskii Military Space Engineering Institute (now, the Mozhaiskii Military Space Engineering Academy). In 1988, he defended the Candidate of Science (Engineering) Dissertation. Currently, he is working as the Chief of the Second Research Department at the FGUP State Research Institute of Applied Problems. The circle of scientific interests involves the creation of program-apparatus complexes, digital processing of signals, analysis of images, and pattern recognition. Chikhonadskikh has more than 70 publications, including three articles and three monographs (two in partnership). Aleksandr Viktorovich Koryakin. Born in 1959. In 1982, he graduated the Dnepropetrovsk State University. In 2002, he defended the Doctor of Science (Engineering) Dissertation. Currently, he is working as the Senior Researcher at the FGUP State Research Institute of Applied Problems. The circle of scientific interests involves the digital processing of signals, analysis of images, pattern recognition, and creation of software-apparatus complexes. Koryakin has more than 100 publications, including three monographs. Inga Yur’evna Terent’eva. Born in 1978. In 2000, she graduated the North-West Academy of State Service (St. Petersburg), and in 2004, she graduated the Post-Graduate Education at the Institute of the Human Brain, Russian Academy of Sciences. In 2005, she defended the Candidate of Science (Psychological) Dissertation. Currently, she is working as the Chief of Laboratory at the FGUP State Research Institute of Applied Problems. The circle of scientific interests involves the analysis of biometric data, pattern recognition, and neural networks. Terent’eva has 23 publications, including eight articles.     

Stabilization of the compressed data formation rate in hierarchical image compression

A new method is proposed for stabilizing the rate of compressed data formation in the case of hierarchical image compression. The method is based on using various values of the control parameter (maximum error) for various scale levels of image representation and for error correction at the last level depending on the received compressed information content. Gashnikov Mikhail Valer’evich (b. 1975) graduated from the Samara State Aerospace University (SSAU) in 1998. In 2004, he defended his Ph.D. thesis in engineering. At present, Gashnikov is an associate lecturer at the Department of Geoinformatics (SSAU). The scope of his scientific interests includes image processing, compression, and statistical coding. He has 30 publications, including 12 papers and a monograph (with co-authors). He is a member of the Russian Association for Image Recognition and Analysis. Glumov Nikolai Ivanovich (b. 1962) graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1985. In 1994, he defended his Ph.D. thesis in engineering. At present, Glumov is a senior scientist at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include image processing and recognition, image compression, and simulation of digital image formation systems. He has more than 60 publications, including 20 articles and a monograph (with co-authors). He is a member of the Russian Association of Image Recognition and Analysis. Sergeyev Vladislav Viktorovich (b. 1951) graduated from the Kuibyshev Aviation Institute (now Samara State Aerospace University) in 1974. In 1993, he defended his D.Sc. (eng.) thesis. At present, V.V. Sergeyev heads the Laboratory of Mathematical Methods of Image Processing at the Institute of Image Processing Systems, Russian Academy of Sciences. His scientific interests include digital signal processing, image analysis, image recognition, and geoinformatics. He has more than 150 publications, including 40 articles and two monographs (with co-authors). V.V. Sergeyev is the chairman of the Povolzh’e Division of the Russian Association for Pattern Recognition and Image Analysis and a Corresponding Member of the Russian Academy of Ecology and the Academy of Engineering Sciences of the Russian Federation, a member of SPIE (International Society for Optical Engineering), and a winner of the Samara Regional Award in Science and Engineering.     

Construction and Application of Hybrid Wavelet and Other Parametric Orthogonal Transforms

We introduce new methods for construction and implementation of various parametric and hybrid orthogonal transforms, including generalized Haar-like, Daubechies, and Coiflet wavelet transforms. The corresponding fast algorithms of computations are briefly discussed and the variance properties of these transforms in analyzing 1-st order Markov processes are investigated. The designed hybrid transforms can be useful in various specific signal processing applications where combining properties of Hadamard and wavelet transforms may be of particular benefit. We also present some numerical results pertaining to image zonal and threshold coding using these hybrid transforms and compare their efficacy with those of traditional orthogonal transforms.Hakob Sarukhanyan received his M.S. degree in Applied Mathematics from Yerevan State University in 1973, and his Ph.D. and D.Sc. degrees in Technical Sciences from the National Armenian Academy of Sciences (NAAS) in 1982 and 1999 accordingly. He has worked as a faculty in the Department of Applied Mathematics at Yerevan State University in 1968–73, and as a junior and senior researcher in the Laboratory of Image Processing Systems at the Institute for Informatics and Automation Problems (IIAP) of the NAAS in 1973–93. He has been the Head of the above Laboratory since 1993 and has been elected a member of the Doctoral Council at IIAP in 2000. He has been a visiting professor at the Tampere Institute of Technology, Finland, in 1999–2001. He is a recipient of research grants from various European funding agencies as well as from the US Civilian and Research Foundation (sponsored by the NSF and the US Department of State). His main research interests are in construction of Hadamard matrices and their applications in wireless communications, combinatorics theory, and fast orthogonal transforms for image processing. He is the author of more than 70 scientific publications in major scientific media.Arthur Petrosian received his M.S. Summa Cum Laude degree in Mathematics from Moscow State University in 1983, and a Ph.D. in Applied Mathematics from the Institute for Problems of Informatics & Automation of the National Armenian Academy of Sciences in 1989. He was a visiting scientist at the Institute of General and Physical Chemistry at Belgrade University, Yugoslavia (1991), an NIH supported postdoctoral fellow at the University of Michigan, Ann Arbor (1992–93), and a research instructor at the Medical College of Ohio, Toledo (1993–94). He joined Texas Tech University Health Sciences Center as an Assistant Professor in 1994, and was appointed as an Adjunct Professor of Mathematics and Electrical and Computer Engineering at Texas Tech University in 1995. He was promoted to the Associate Professor level at Texas Tech University Health Sciences Center in 2000. While at Texas Tech, he received a number of research grant awards to conduct research in EEG signal processing and in biomedical signal/image compression, including from the Federal Administration on Aging, Alzheimers Association, and the US Civilian and Research Foundation (sponsored by the NSF and the US Department of State, to promote cooperative research between the wavelet theory groups in United States and ex-USSR). He is a Senior Member of IEEE and a past member of the New York Academy of Sciences. He is currently a Scientific Review Administrator in the Surgery, Biomedical Imaging, and Bioengineering integrated review group at the National Institutes of Health, US Department of Health and Human Services.     

Image denoising with neighbour dependency and customized wavelet and threshold

Image denoising by means of wavelet transforms has been an active research topic for many years. For a given noisy image, which kind of wavelet and what threshold we use should have significant impact on the quality of the denoised image. In this paper, we use Simulated Annealing to find the customized wavelet filters and the customized threshold corresponding to the given noisy image at the same time. Also, we propose to consider a small neighbourhood around the customized wavelet coefficient to be thresholded for image denoising. Experimental results show that our approach is better than VisuShrink, our NeighShrink with fixed wavelet, and the wiener2 filter that is available in Matlab Image Processing Toolbox. In addition, our NeighShrink with fixed wavelet already outperforms VisuShrink for all the experiments.     

An adaptive edge-preserving image denoising technique using patch-based weighted-SVD filtering in wavelet domain

Image denoising has always been one of the standard problems in image processing and computer vision. It is always recommendable for a denoising method to preserve important image features, such as edges, corners, etc., during its execution. Image denoising methods based on wavelet transforms have been shown their excellence in providing an efficient edge-preserving image denoising, because they provide a suitable basis for separating noisy signal from the image signal. This paper presents a novel edge-preserving image denoising technique based on wavelet transforms. The wavelet domain representation of the noisy image is obtained through its multi-level decomposition into wavelet coefficients by applying a discrete wavelet transform. A patch-based weighted-SVD filtering technique is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method achieves very impressive gain in denoising performance.     

Hybrid semi-blind image watermarking in redundant wavelet domain

Multimedia Tools and Applications - Image watermarking in wavelet domain has been found useful for copyright protection and rightful ownership. Classical wavelet transforms, like discrete wavelet...