scholarly journals Acoustic impedance estimation using a gradient-based algorithm with total variation semi-norm regularization

2017 ◽  
Author(s):  
Daniel O. Pérez ◽  
Danilo R. Velis ◽  
Juan I. Sabbione
Keyword(s):  
Total Variation ◽  
Acoustic Impedance ◽  
Gradient Based

scholarly journals Structure tensor adaptive total variation for image restoration

2019 ◽  
pp. 1147-1156 ◽  
Author(s):  
SURYA PRASATH ◽  
DANG NH THANH
Keyword(s):  
Total Variation ◽  
Visual Cues ◽  
Noise Removal ◽  
Structure Tensor ◽  
Total Variation Regularization ◽  
Regularization Methods ◽  
Edge Preservation ◽  
Gradient Based ◽  
Adaptive Total Variation ◽  
Variational Regularization

Image denoising and restoration is one of the basic requirements in many digital image processing systems. Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.

scholarly journals Seismic absolute acoustic impedance inversion with L1 norm reflectivity constraint and combined first- and second-order total variation regularizations

2019 ◽  
Vol 16 (4) ◽  
pp. 773-788 ◽  
Author(s):  
Song Guo ◽  
Huazhong Wang
Keyword(s):  
Total Variation ◽  
Acoustic Impedance ◽  
A Priori ◽  
Intermediate Frequency ◽  
Geological Structure ◽  
Second Order ◽  
L1 Norm ◽  
Split Bregman ◽  
Linear Inversion ◽  
First Order

Abstract Absolute acoustic impedance (AI) is generally divided into background AI and relative AI for linear inversion. In practice, the intermediate frequency components of the AI model are generally poorly reconstructed, so the estimated AI will suffer from an error caused by the frequency gap. To remedy this error, a priori information should be incorporated to narrow down the gap. With the knowledge that underground reflectivity was sparse, we solved an L1 norm constrained problem to extend the bandwidth of the reflectivity section, and an absolute AI model was then estimated with broadband reflectivity section and given background AI. Conventionally, the AI model is regularized with the total variation (TV) norm because of its blocky feature. However, the first-order TV norm that leads to piecewise-constant solutions will cause staircase errors in slanted and smooth regions in the inverted AI model. To better restore the smooth variation while preserving the sharp geological structure of the AI model, we introduced a second-order extension of the first-order TV norm and inverted the absolute AI model with combined first- and second-order TV regularizations. The algorithm used to solve the optimization problem with the combined TV constraints was derived based on split-Bregman iterations. Numerical experiments that were tested on the Marmousi AI model and 2D stacked field data illustrated the effectiveness of the sparse constraint with respect to shrinking the frequency gaps and proved that the proposed combined TV norms had better performances than those with conventional first-order TV norms.

Omni-gradient-based total variation minimisation for sparse reconstruction of omni-directional image

2014 ◽  
Vol 8 (7) ◽  
pp. 397-405 ◽  
Author(s):  
Jingtao Lou ◽  
Yu Liu ◽  
Yongle Li ◽  
Maojun Zhang ◽  
Shuren Tan
Keyword(s):  
Total Variation ◽  
Sparse Reconstruction ◽  
Gradient Based ◽  
Directional Image

Acoustic-Reflex Dynamics for Pulsed Signals

1978 ◽  
Vol 21 (2) ◽  
pp. 295-308
Author(s):  
Terry L. Wiley ◽  
Raymond S. Karlovich
Keyword(s):  
Duty Cycle ◽  
Acoustic Impedance ◽  
Normal Hearing ◽  
Broadband Noise ◽  
Threshold Shift ◽  
Temporary Threshold Shift ◽  
Acoustic Reflex ◽  
Stapedius Muscle ◽  
Duty Cycles

Contralateral acoustic-reflex measurements were taken for 10 normal-hearing subjects using a pulsed broadband noise as the reflex-activating signal. Acoustic impedance was measured at selected times during the on (response maximum) and off (response minimum) portions of the pulsed activator over a 2-min interval as a function of activator period and duty cycle. Major findings were that response maxima increased as a function of time for longer duty cycles and that response minima increased as a function of time for all duty cycles. It is hypothesized that these findings are attributable to the recovery characteristics of the stapedius muscle. An explanation of portions of the results from previous temporary threshold shift experiments on the basis of acoustic-reflex dynamics is proposed.

Gradient based system-level diagnosis

2007 ◽  
Vol 51 (1-2) ◽  
pp. 43
Author(s):  
Balázs Polgár ◽  
Endre Selényi
Keyword(s):  
System Level ◽  
Gradient Based
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