PSF estimation using total variation regularization and shock filter for blind deconvolution

2017 ◽  
Author(s):  
Hiroki Senshiki ◽  
Satoshi Motohashi ◽  
Tomio Goto ◽  
Haifeng Chen ◽  
Reo Aoki
Keyword(s):  
Total Variation ◽  
Blind Deconvolution ◽  
Total Variation Regularization ◽  
Shock Filter ◽  
Psf Estimation

In vivo blind-deconvolution photoacoustic ophthalmoscopy with total variation regularization

2018 ◽  
Vol 11 (9) ◽  
pp. e201700360 ◽  
Author(s):  
Deyan Xie ◽  
Qin Li ◽  
Quanxue Gao ◽  
Wei Song ◽  
Hao F. Zhang ◽  
...  
Keyword(s):  
Total Variation ◽  
Blind Deconvolution ◽  
Total Variation Regularization

scholarly journals Total Variation Regularization Algorithms for Images Corrupted with Different Noise Models: A Review

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Paul Rodríguez
Keyword(s):  
Total Variation ◽  
Blind Deconvolution ◽  
Numerical Algorithms ◽  
Total Variation Regularization ◽  
General Technique ◽  
Denoising Method ◽  
Tv Regularization ◽  
Mixed Noise ◽  
Map Estimator ◽  
Noise Models

Total Variation (TV) regularization has evolved from an image denoising method for images corrupted with Gaussian noise into a more general technique for inverse problems such as deblurring, blind deconvolution, and inpainting, which also encompasses the Impulse, Poisson, Speckle, and mixed noise models. This paper focuses on giving a summary of the most relevant TV numerical algorithms for solving the restoration problem for grayscale/color images corrupted with several noise models, that is, Gaussian, Salt & Pepper, Poisson, and Speckle (Gamma) noise models as well as for the mixed noise scenarios, such the mixed Gaussian and impulse model. We also include the description of the maximum a posteriori (MAP) estimator for each model as well as a summary of general optimization procedures that are typically used to solve the TV problem.

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.

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