Image Deconvolution with Simulated Annealing Method

Raittinen, Harri; Kaski, Kimmo

doi:10.1088/0031-8949/1990/t33/022

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Often, an algebraic model is used to develop a filter to aid in this process. Then, parameters of the model must be fit to data, and here SQ techniques have been quite successful [43,44]. The models often are not very nonlinear, but they are high dimensional.…”

Section: Imagingmentioning

confidence: 99%

Simulated annealing: Practice versus theory

Ingber¹

1993

Mathematical and Computer Modelling

951

500

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Simulated annealing (SA) presents an optimization technique with several striking positive and negative features. Perhaps its most salient feature, statistically promising to deliver an optimal solution, in current practice is often spurned to use instead modified faster algorithms, "simulated quenching" (SQ). Using the author's Adaptive Simulated Annealing (ASA) code, some examples are given which demonstrate how SQ can be much faster than SA without sacrificing accuracy.

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Section: Imagingmentioning

confidence: 99%

Simulated annealing: Practice versus theory

Ingber¹

1993

Mathematical and Computer Modelling

951

500

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“…As described in [28], SA offers an alternative approach to the original architecture of the ensemble enhancer within AMC. SA has been utilized to study the traveling salesman problem, in circuit design, the design of decision trees, in data analysis, imaging and neural networks, as well as in areas of biology, physics, finance, and the military, among others [52], [53], [54], [55], [56], [57], [58], [59], [60], [61]. As SA is inherently sequential, significant research has been conducted to not only increase its serial efficiency, but also to parallelize the algorithm while maintaining convergence properties or, at the very least, induce minimal errors in the algorithm.…”

Section: A State Of the Art: Parallel Samentioning

confidence: 99%

Efficient Adaptive Monte Carlo Uncertainty Forecasting for High Dimensional Nonlinear Dynamic Systems

VanFossen

Kumar

2023

IEEE Access

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The problem of uncertainty forecasting for complex dynamical systems in the framework of particle methods can be effectively addressed through the solution methodology known as adaptive Monte Carlo (AMC). Monte Carlo (MC) methods involve discretizing the initial probability density function (pdf) followed by forward propagation of particles through system dynamics to obtain an approximate particle representation of the evolved state uncertainty. While simple to implement, MC faces questions surrounding transient statistical consistency and rate of convergence. AMC (adopted here) addresses these issues on-the-fly using defined bounds on estimation accuracy alongside ensemble enrichment routines. This paper presents several improvements in the ensemble enrichment module of AMC. The AMC platform is re-engineered to include the novel implementation of a parallel global stochastic optimization routine in conjunction with additional module enhancements that work together towards the goal of efficient forecasting. Moreover, the efficacy of algorithms utilized within every submodule of AMC are detailed and improved upon under the framework of arithmetic minimization and parallelization. Each submodule is profiled to determine computational bottlenecks, optimized or replaced with more efficient methods such as simulated annealing (SA), and parallelized ultimately leading to a clock time reduction of 200 − 400% for benchmark entry descent and landing, Lorenz-96, and Lotka-Volterra models.

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“…The quenching parameter is of the same order as D and makes the simulated annealing lose its property of statistical convergence to the global minimum, although in practice it might still be among the best algorithms for a given system (Ingber & Rosen, 1992). Indeed, it has been successfully applied to a number of complex problems with large dimension (Raittinen & Kaski, 1990;Roy et al, 2005).…”

Section: 1029/2018jb015883mentioning

confidence: 99%

Seafloor Topography Estimation From Gravity Gradients Using Simulated Annealing

2018

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Inferring seafloor topography from gravity anomaly currently is the dominant method to obtain a global view of the oceans. Standard techniques rely on an approximate, linear relationship between topography and gravity, which is valid only if the local topography is smooth compared with the regional topography, so the estimation accuracy in the very rugged areas is low. Current methods can be improved by removing the linear approximation and estimating the topography through simulated annealing and by using gravity gradiometry that is more sensitive to topography at short wavelengths than the gravity anomaly. Simulated annealing is a global optimization technique that can process nonlinear inverse problems. It is developed to estimate the seafloor depths by minimizing the difference between the observed and forward‐computed vertical gravity gradients. The method is tested on altimetry‐derived gravity gradients in a 2∘×2∘ area of rugged seafloor topography in the West Pacific Ocean and results in estimates with a root‐mean‐square error of ±236 m. Compared to estimates from an existing model obtained by standard techniques this represents an accuracy improvement of 22%.

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Image Deconvolution with Simulated Annealing Method

Cited by 8 publications

References 3 publications

Simulated annealing: Practice versus theory

Simulated annealing: Practice versus theory

Efficient Adaptive Monte Carlo Uncertainty Forecasting for High Dimensional Nonlinear Dynamic Systems

Seafloor Topography Estimation From Gravity Gradients Using Simulated Annealing

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