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13th IEEE International Conference on Industrial Informatics (INDIN)

作者： Situ, Guohai Suo, Jinli Dai, Qionghai Chinese Acad Sci Shanghai Inst Opt & Fine Mech Shanghai 201800 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781479966493

It is well known that the phase contains more important information about the field in comparison with the amplitude. Therefore the imaging of phase is encountered in many branches of modern science and engineering. Direct measurement of the phase is easy in the long wavelength regime of the electromagnetic spectrum, but is difficult in the short regime such as the visible light due to the limited bandwidth of imaging sensors. One must employ computational techniques to extract the phase from the captured intensity. So far many methods have been proposed for this task. These algorithms can be basically classified into three categories: Holography, deterministic algorithms such as the transport of intensity equation, and iterative algorithms such as the Gerchberg-Saxton-Fienup-type algorithm. Each of these algorithms has its own advantages and disadvantages. This paper mainly focuses on the our previous works on iterative phase retrieval techniques, and their applications in the calculation of computer-generated holograms, microscopic imaging, and optical signal processing.

关键词： Phase Phase measurement Electromagnetic spectrum Optical signal processing iterative algorithms iterative algorithms retrieval Visible Light Sensors (image) Transport equations

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iterative Non-negative Deconvolution algorithms with α-Divergence 7

Iterative Non-negative Deconvolution Algorithms with α-Dive...

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7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Teng, Yueyang Yan, Jing Kang, Yan Northeastern Univ Sino Dutch Biomed & Informat Engn Sch Boston MA 02115 USA

ISBN: (纸本)9781479986460

This paper presents a class of iterative deconvolution algorithms based on Amari's alpha-divergence in the condition of non-negativity constraints. The alpha-divergence is actually a family of divergences indexed by alpha (-8,+8) that can measure the discrepancy between two distributions or nonnegative sequences. We consider it to model the difference between the deblurred image and its estimate. By iterative minimization, a general update rule is derived by constructing a surrogate function. The well-known Richardson-Lucy (RL) algorithm arises as a special case of our method. The proposed algorithms monotonically decrease the cost functions and automatically meet the non-negativity constraints. The experiments were performed on both simulated and real medical images to investigate the interesting and useful behavior of the algorithms when different parameters (alpha) were used. The results showed that some chosen ones exhibited much better performance than the RL algorithm.

关键词： alpha-divergence deconvolution iterative algorithms surrogate function

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A Myth of iterative Image Reconstruction algorithms

A Myth of Iterative Image Reconstruction Algorithms

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IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) / 24th International Symposium on Room-Temperature Semiconductor X-Ray and Gamma-Ray Detectors

作者： Zeng, Gengsheng L. Weber State Univ Dept Engn Ogden UT 84408 USA Univ Utah Dept Radiol & Imaging Sci Salt Lake City UT 84108 USA

ISBN: (纸本)9781538622827

The FBP (filtered backprojection) algorithm reduces image noise by smoothing the image. The iterative algorithm reduces image noise by noise weighting and regularization. A myth is that the iterative algorithm is able to reduce the noise without sacrificing image resolution. This paper uses counter examples to show that this myth is not true. The truth is that the iterative algorithm suppresses image noise by sacrificing image resolution as well;its performance may be better than the windowed FBP. The myth is caused by the comparison method that compares the iterative algorithm with the conventional FBP algorithm instead of the windowed FBP algorithm.

关键词： Image reconstruction Filtering algorithms Low pass filters iterative algorithms Spatial resolution

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A Selection of Starting Points for iterative Position Estimation algorithms Using Feedforward Neural Networks

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SENSORS 2024年第2期24卷 332-332页

作者： Sadowski, Jaroslaw Stefanski, Jacek Gdansk Univ Technol Fac Elect Telecommun & Informat PL-80233 Gdansk Poland

This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.

关键词： radio localization position estimation iterative algorithms feedforward neural network

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Two iterative image restoration algorithms with applications to nuclear medicine

Two iterative image restoration algorithms with applications...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： C. Charalambous F.K. Ghaddar K. Kouris Kuwait University Kuwait The Cyprus Institute of Neurology and Genetics Nicosia Cyprus Cyprus Inst. of Neurol. & Genetics Nicosia Cyprus

Two methods for recovering an image that has been degraded while being processed are presented. The first algorithm reduces the problem to the computation of a few discrete Fourier transforms. The second algorithm with weight matrices included enables the handling of edges and flat regions in the image in a pleasing manner for the human visual system. These algorithms have been applied to nuclear medicine images.< >

关键词： Image restoration iterative algorithms Nuclear medicine Lagrangian functions Degradation Humans Visual system Additive noise iterative methods Nervous system

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On implementation choices for iterative improvement partitioning algorithms

On implementation choices for iterative improvement partitio...

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European Design Automation Conference

作者： L.W. Hagen J.-H. Huang A.B. Kahng Department of Computer Science University of California Los Angeles Los Angeles CA USA Cadence Design Systems Inc. San Jose CA USA

iterative improvement partitioning algorithms such as those due to Fiduccia and Mattheyses (1982) and Krishnamurthy (1984) exploit an efficient gain bucket data structure in selecting modules that are moved from one partition to the other. In this paper, we investigate three gain bucket implementations and their effect on the performance of the Fiduccia-Mattheyses partitioning algorithm. Surprisingly, selection from gain buckets maintained as Last-In-First-Out (LIFO) stacks leads to significantly better results than selection from gain buckets maintained randomly or as First-In-First-Out (FIFO) queues. Our experiments show that LIFO buckets result in a 35% improvement over random buckets and a 42% improvement over FIFO buckets. Furthermore, eliminating randomization from the bucket selection is of greater benefit to Fiduccia-Mattheyses performance than adding the Krishnamurthy gain vector. By combining insights from the LIFO gain buckets with those of Krishnamurthy's original work, a new higher-level gain formulation is proposed. This alternative formulation results in a further 16% reduction in the average cut cost when compared directly to the Krishnamurthy formulation for higher-level gains, assuming LIFO organization for the gain buckets.

关键词： Partitioning algorithms iterative algorithms Circuits Cost function iterative methods Computer science Algorithm design and analysis Data structures Performance gain Production

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iterative multiframe super-resolution algorithms for atmospheric turbulence-degraded imagery

Iterative multiframe super-resolution algorithms for atmosph...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： D.G. Sheppard B.R. Hunt M.W. Marcellin Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA

algorithms for image recovery with super-resolution from sequences of short-exposure images are presented. Both deconvolution from wavefront sensing (DWFS) and blind deconvolution are explored. A multiframe algorithm is presented for DWFS which is based on maximum a posteriori (MAP) formulation. A multiframe blind deconvolution algorithm is presented based on a maximum likelihood formulation with strict constraints incorporated using nonlinear reparameterizations. Quantitative simulation of imaging through atmospheric turbulence and wavefront sensing are used to demonstrate the super-resolution performance of the algorithms.

关键词： Image resolution iterative algorithms Spatial resolution Deconvolution Optical sensors Nonlinear optics Optical attenuators Atmospheric waves Equations Degradation

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iterative color image restoration algorithms

Iterative color image restoration algorithms

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： A.K. Katsaggelos J.K. Paik Department of Electrical Engineering and Computer Science The Technological Institute Northwestern University Evanston IL USA

iterative techniques for restoring colour images are presented. These iterative algorithms have been developed for the restoration of monochromatic images. The colour images are modeled as three spatially related monochromatic images or channels. The interchannel correlation is incorporated into the iterative restoration algorithms in an indirect and in a direct way. Experimental results with simulated and real photographically blurred images are presented. Based on experiments the authors conclude that the incorporation of the interchannel correlation into the algorithms does not necessarily improve the quality of the restored images over the algorithms that ignore the interchannel correlation, but they result in considerable computation savings.< >

关键词： Color Image restoration iterative algorithms Degradation Optical noise Colored noise Magnetic separation Computational modeling Error correction Digital recording

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Synchronization algorithms for iterative demodulated M-ary DS-CDMA systems

Synchronization algorithms for iterative demodulated M-ary D...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： Pei Xiao E. Strom Communication Systems Group Department of Signals and Systems Chalmers University of Technology Goteborg Sweden

In this paper, we developed several algorithms to combat the impact of synchronization errors on demodulating M-ary orthogonal signaling formats in asynchronous DS-CDMA systems. The system under study resembles the uplink of an IS-95 system. The channel is assumed to be a time-varying flat Rayleigh-fading channel. Investigation shows that synchronization errors severely deteriorate the performance of multi-user detectors. We proposed an adaptive algorithm to estimate the errors in synchronization. Based on this information, remedial actions are taken to alleviate the performance degradation caused by sampling the received signals at the incorrect timing. Simulation results show considerable capacity gains when the proposed algorithms are performed to erroneously sampled signals.

关键词： iterative algorithms Multiaccess communication Degradation Interference cancellation Interference suppression Multiple access interference Fading Channel estimation Delay estimation Robustness

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iterative image enhancement algorithms for multi-layer specimens in scanning tomographic acoustic microscopy

Iterative image enhancement algorithms for multi-layer speci...

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IEEE Symposium (IUS) Ultrasonics

作者： B.L. Douglas H. Lee Center for Information Processing Research Department of Electrical and Computer Engineering University of California Santa Barbara USA University of Illinois Urbana USA

A performance evaluation of three iterative enhancement techniques is presented. The reconstruction obtained with the backward propagation algorithm is used as the initial estimate for these recursive procedures. The results indicate that the estimates can be improved consistently by using Newton's method, the conjugate gradient method, or pixel-by-pixel optimization. In addition to simulated data, experimental data samples have been applied to this analysis to demonstrate the capability, effectiveness, and convergence properties of these techniques. In the computer simulations at low signal-to-noise ratios (SNRs), the conjugate gradient method was the best, and at high SNRs Newton's method was superior.< >

关键词： Image enhancement iterative algorithms Newton method Gradient methods Image reconstruction Recursive estimation Optimization methods Computational modeling Analytical models Convergence

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