A robust image registration method based on total variation regularization under complex illumination changes

2016 ◽  
Vol 134 ◽  
pp. 89-107 ◽  
Author(s):  
Khadijeh Aghajani ◽  
Mohammad T. Manzuri ◽  
Rohollah Yousefpour
Keyword(s):  
Image Registration ◽  
Total Variation ◽  
Total Variation Regularization ◽  
Registration Method ◽  
Illumination Changes ◽  
Robust Image

scholarly journals Isotropic Total Variation Regularization of Displacements in Parametric Image Registration

2017 ◽  
Vol 36 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Valery Vishnevskiy ◽  
Tobias Gass ◽  
Gabor Szekely ◽  
Christine Tanner ◽  
Orcun Goksel
Keyword(s):  
Image Registration ◽  
Total Variation ◽  
Total Variation Regularization ◽  
Parametric Image

Robust image registration using subspace method in Radon transform domain

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 645-651
Author(s):  
Ning Wei ◽  
Yu He ◽  
Junqing Liu ◽  
Peng Chen
Keyword(s):  
Image Registration ◽  
Radon Transform ◽  
Median Filter ◽  
Least Square ◽  
Phase Correlation ◽  
Transform Domain ◽  
Registration Method ◽  
Content Type ◽  
Structural Differences ◽  
Robust Image

Purpose The purpose of this paper is to represent a robust image registration method to align noisy and deformed images in their Radon transform domain. Due to the limitation of imaging mechanism, the images are often highly noisy. Even worse, the objects in images have structural differences from time to time. Design/methodology/approach To eliminate these degressions, the proposed method is equipped with subspace-based power spectrum analysis algorithm for rotation estimation and a new global median filter least square algorithm for displacement computation. Findings Experiments on strongly noisy and degenerated images show that the proposed method exhibits better accuracy and robustness than phase correlation-based method. In addition, the method can also be applied to multi-modal registration, where the results are comparable to mutual information method but spending much less time. Originality/value A robust image registration method is proposed, which has better performance than traditional methods.

scholarly journals An Improved Particle Swarm Optimization Based on Total Variation Regularization and Projection Constraint with Applications in Ground-Penetrating Radar Inversion: A Model Simulation Study

2021 ◽  
Vol 13 (13) ◽  
pp. 2514
Author(s):  
Qianwei Dai ◽  
Hao Zhang ◽  
Bin Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Total Variation ◽  
Ground Penetrating Radar ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Total Variation Regularization ◽  
Interface Model ◽  
Swarm Optimization ◽  
Tv Regularization ◽  
Ground Penetrating

The chaos oscillation particle swarm optimization (COPSO) algorithm is prone to binge trapped in the local optima when dealing with certain complex models in ground-penetrating radar (GPR) data inversion, because it inherently suffers from premature convergence, high computational costs, and extremely slow convergence times, especially in the middle and later periods of iterative inversion. Considering that the bilateral connections between different particle positions can improve both the algorithmic searching efficiency and the convergence performance, we first develop a fast single-trace-based approach to construct an initial model for 2-D PSO inversion and then propose a TV-regularization-based improved PSO (TVIPSO) algorithm that employs total variation (TV) regularization as a constraint technique to adaptively update the positions of particles. B by adding the new velocity variations and optimal step size matrices, the search range of the random particles in the solution space can be significantly reduced, meaning blindness in the search process can be avoided. By introducing constraint-oriented regularization to allow the optimization search to move out of the inaccurate region, the premature convergence and blurring problems can be mitigated to further guarantee the inversion accuracy and efficiency. We report on three inversion experiments involving multilayered, fluctuated terrain models and a typical complicated inner-interface model to demonstrate the performance of the proposed algorithm. The results of the fluctuated terrain model show that compared with the COPSO algorithm, the fitness error (MAE) of the TVIPSO algorithm is reduced from 2.3715 to 1.0921, while for the complicated inner-interface model the fitness error (MARE) of the TVIPSO algorithm is reduced from 1.9539 to 1.5674.

A new nonlinear reconstruction method based on total variation regularization of neutron penumbral imaging

2011 ◽  
Vol 82 (9) ◽  
pp. 093504 ◽  
Author(s):  
Weixin Qian ◽  
Shuangxi Qi ◽  
Wanli Wang ◽  
Jinming Cheng ◽  
Dongbing Liu
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Reconstruction Method ◽  
Penumbral Imaging
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