Direction Parameter Identification of Motion-Blurred Image Based on Three Second Order Frequency Moments

2009 ◽  
Author(s):  
Yiping Wang ◽  
Xinsheng Huang ◽  
Peng Jia
Keyword(s):  
Parameter Identification ◽  
Second Order ◽  
Frequency Moments ◽  
Blurred Image

Defocus blurred image restoration by minimizing second-order central moment

2011 ◽  
Author(s):  
Sheng Zhong ◽  
Mingzhi Jin ◽  
Luxin Yan ◽  
Tianxu Zhang
Keyword(s):  
Image Restoration ◽  
Central Moment ◽  
Second Order ◽  
Blurred Image

scholarly journals A COMPARISON OF ONE- AND TWO-SIDED KRYLOV–ARNOLDI PROJECTION METHODS FOR FULLY COUPLED, DAMPED STRUCTURAL-ACOUSTIC ANALYSIS

2013 ◽  
Vol 21 (02) ◽  
pp. 1350004 ◽  
Author(s):  
R. SRINIVASAN PURI ◽  
DENISE MORREY
Keyword(s):  
Coupled System ◽  
Dimensional Subspace ◽  
Second Order ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Frequency Moments ◽  
Arnoldi Algorithm ◽  
Higher Dimensional ◽  
Fully Coupled

The two-sided second-order Arnoldi algorithm is used to generate a reduced order model of two test cases of fully coupled, acoustic interior cavities, backed by flexible structural systems with damping. The reduced order model is obtained by applying a Galerkin–Petrov projection of the coupled system matrices, from a higher dimensional subspace to a lower dimensional subspace, whilst preserving the low frequency moments of the coupled system. The basis vectors for projection are computed efficiently using a two-sided second-order Arnoldi algorithm, which generates an orthogonal basis for the second-order Krylov subspace containing moments of the original higher dimensional system. The first model is an ABAQUS benchmark problem: a 2D, point loaded, water filled cavity. The second model is a cylindrical air-filled cavity, with clamped ends and a load normal to its curved surface. The computational efficiency, error and convergence are analyzed, and the two-sided second-order Arnoldi method shows better efficiency and performance than the one-sided Arnoldi technique, whilst also preserving the second-order structure of the original problem.

scholarly journals Adjoint accuracy for the full Stokes ice flow model: limits to the transmission of basal friction variability to the surface

2014 ◽  
Vol 8 (2) ◽  
pp. 721-741 ◽  
Author(s):  
N. Martin ◽  
J. Monnier
Keyword(s):  
Friction Coefficient ◽  
Parameter Identification ◽  
Adjoint Method ◽  
Synthetic Data ◽  
Adjoint Problem ◽  
Second Order ◽  
The Self ◽  
Algorithmic Differentiation ◽  
Numerical Assessment ◽  
Linear Friction

Abstract. This work focuses on the numerical assessment of the accuracy of an adjoint-based gradient in the perspective of variational data assimilation and parameter identification in glaciology. Using noisy synthetic data, we quantify the ability to identify the friction coefficient for such methods with a non-linear friction law. The exact adjoint problem is solved, based on second-order numerical schemes, and a comparison with the so-called "self-adjoint" approximation, neglecting the viscosity dependence on the velocity (leading to an incorrect gradient), common in glaciology, is carried out. For data with a noise of 1%, a lower bound of identifiable wavelengths of 10 ice thicknesses in the friction coefficient is established, when using the exact adjoint method, while the "self-adjoint" method is limited, even for lower noise, to a minimum of 20 ice thickness wavelengths. The second-order exact gradient method therefore provides robustness and reliability for the parameter identification process. In another respect, the derivation of the adjoint model using algorithmic differentiation leads to the formulation of a generalization of the "self-adjoint" approximation towards an incomplete adjoint method, adjustable in precision and computational burden.

Online Parameter Identification of Second-Order Systemic Circulation Model Using the Delta Operator

2002 ◽  
Vol 26 (11) ◽  
pp. 967-970 ◽  
Author(s):  
Ryo Kosaka ◽  
Yoshiyuki Sankai ◽  
Tomoaki Jikuya ◽  
Takashi Yamane ◽  
Tatsuo Tsutsui
Keyword(s):  
Parameter Identification ◽  
Circulation Model ◽  
Systemic Circulation ◽  
Second Order ◽  
Delta Operator

Blurred image restoration using the type of blur and blur parameter identification on the neural network

2002 ◽  
Author(s):  
Igor N. Aizenberg ◽  
Constantine Butakoff ◽  
Viktor N. Karnaukhov ◽  
Nikolay S. Merzlyakov ◽  
Olga Milukova
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Image Restoration ◽  
The Neural Network ◽  
Blurred Image
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