Targeted reconstruction is reconstruction of a portion of an object, even when sufficient data are available to reconstruct the entire object. It is used in CT to zoom in on a region using smaller pixels without the b...
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Targeted reconstruction is reconstruction of a portion of an object, even when sufficient data are available to reconstruct the entire object. It is used in CT to zoom in on a region using smaller pixels without the burden of reconstructing a larger array. This is straightforward when using analytic algorithms. It is less apparent how to perform this kind of targeted reconstruction using iterative algorithms, since these algorithms involve reprojecting the entire object for comparison with the data. In this work, we consider two approaches to this problem. One is a simple extension of an approach previously proposed by Ziegler but which better preserves the statistical properties of the raw data. An initial analytic reconstruction is performed, the ROI of interest is removed, and what remains is reprojected to obtain an estimate of the contributions of the area outside the ROI to the projections. Then this estimate is added to the projections of the ROI at each iteration, allowing for comparison to be made with the original, unmodified data. The second approach treats the set of non-ROI projections as a second vector of unknowns, in addition to the pixel values within the ROI. We then seek to maximize a joint penalized likelihood objective function and we do so using an alternating update strategy that is guaranteed to monotonically increase the objective function.
iterative deconvolution algorithms exhibit increased ringing artifacts at higher number of iterations. Quantitative image analysis techniques such as optical flow algorithms are sensitive to these artifacts. We propos...
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iterative deconvolution algorithms exhibit increased ringing artifacts at higher number of iterations. Quantitative image analysis techniques such as optical flow algorithms are sensitive to these artifacts. We propose to use an image quality metric to identify the terminal step of iterative restoration algorithms. Frequency based metrics have difficulties in distinguishing the ringing artifacts from the image features. Spatial analysis techniques require extensive processing making them unsuitable for real-time image quality measurement as needed in iterative image restoration. We propose a novel ringing metric using binary morphological operators and demonstrate the method on the images of random cotton fibers acquired using white light confocal microscope.
This paper presents a detailed analysis of an iterative routing algorithm in which multiple passes are made through a net list, varying the region of crossover on each net after every iteration. This is in contrast to...
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This paper presents a detailed analysis of an iterative routing algorithm in which multiple passes are made through a net list, varying the region of crossover on each net after every iteration. This is in contrast to conventional iterative routing algorithms which incorporate a crossover penalty within the cost function being minimised. Experimental results are presented and, based on these results, new modifications proposed. The modified algorithm is compared to a conventional iterative algorithm and a single pass Lee router. The results illustrate the improved completion rates of iterative algorithms with respect to single pass algorithms. Finally, an element of dynamic routing (ripup and reroute) is introduced to further improve the completion rate of the modified algorithm.
In this paper we analyze the propagation of noise in iterative image reconstruction algorithms. We derive theoretical expressions for the general form of preconditioned gradient algorithms with line searches. The resu...
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In this paper we analyze the propagation of noise in iterative image reconstruction algorithms. We derive theoretical expressions for the general form of preconditioned gradient algorithms with line searches. The results are applicable to a wide range of iterative reconstruction problems, such as emission tomography, transmission tomography, and image restoration. A unique contribution of this paper comparing to our previous work [J. Qi (2003)] is that the line search is explicitly modeled and we do not use the approximation that the gradient of the objective function is zero. As a result, the error in the estimate of noise at early iterations is significantly reduced.
In this paper, we analyze the performances of iterative decoders for linear block codes. In particular, we consider two modifications of the gradient-descent bit flipping (GDBF) algorithm with momentum, where multiple...
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ISBN:
(数字)9798350391060
ISBN:
(纸本)9798350391077
In this paper, we analyze the performances of iterative decoders for linear block codes. In particular, we consider two modifications of the gradient-descent bit flipping (GDBF) algorithm with momentum, where multiple component decoders with different momentum values are concatenated to improve the decoder performance. The learning parameters of the component decoders are obtained by using a Genetic algorithm based on the database of the uncorrectable error patterns of the previous decoder. We present three optimization strategies and provide a comparison with the state-of-the-art decoders. The numerical results are presented on short Bose-Chaudhuri-Hocquenghem (BCH) codes and the channel with additive white Gaussian noise (AWGN).
The authors describe some iterative segmentation algorithms that combine statistical constraints represented in Markov random field models with deterministic constraints imposed by morphological operations. The goal i...
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The authors describe some iterative segmentation algorithms that combine statistical constraints represented in Markov random field models with deterministic constraints imposed by morphological operations. The goal is to produce segmentations that have high probability according to the Markov model and are smooth in the sense of being morphologically open and/or closed. The authors first present several algorithms for binary images, including one that produces a segmentation in which the set of one's is both open and closed. The latter algorithm is then extended to the case of multiregion images to produce a segmentation in which each region is open and closed.< >
It is usually impossible to exactly solve hard optimization problems. One is thus directed to iterative algorithms. In the implementation of these iterative algorithms, some common characteristics can be observed, whi...
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ISBN:
(纸本)9539676932
It is usually impossible to exactly solve hard optimization problems. One is thus directed to iterative algorithms. In the implementation of these iterative algorithms, some common characteristics can be observed, which can be generalized in an object-oriented framework. This can significantly reduce the time needed for implementation of an iterative algorithm. This paper presents a class hierarchy, which can be used as an object-oriented framework for the solutions of iterative problems. Utilizing the options available in object-oriented languages, it separates the class interface from the implementation.
Originally developed for decoding turbo codes, normal factor graphs are a natural setting for the description of iterative techniques for detecting coded signals transmitted on a variety of channels. In addition, they...
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Originally developed for decoding turbo codes, normal factor graphs are a natural setting for the description of iterative techniques for detecting coded signals transmitted on a variety of channels. In addition, they provide a unified framework allowing one to understand the connections among seemingly different detection problems. This tutorial describes the application of normal factor graphs to a number of these problems, such as equalization of coded signals, multi-user detection, decoding of multilevel coded modulation, and reception of space--time coded signals.
The scanning tomographic acoustic microscope (STAM) is an advanced device capable of performing subsurface three-dimensional imaging of microscopic specimens. The resolution limits of this microscope in both range and...
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The scanning tomographic acoustic microscope (STAM) is an advanced device capable of performing subsurface three-dimensional imaging of microscopic specimens. The resolution limits of this microscope in both range and cross-range are established using the spectral coverage technique. It is shown that the range resolution is a function of the acoustic wavelength, and the cross-range resolution is weakly dependent on the operating frequency. Experimental results are compared to theoretical values with complete agreement. iterative algorithms for image enhancement are also examined. These algorithms are applied to experimental data to demonstrate their effectiveness.
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