Quantum Noise Removal in X-Ray Images with Adaptive Total Variation Regularization

Informatica ◽  
2017 ◽  
Vol 28 (3) ◽  
pp. 505-515 ◽  
Author(s):  
V.B. Surya Prasath
Keyword(s):  
Total Variation ◽  
Quantum Noise ◽  
Noise Removal ◽  
Total Variation Regularization ◽  
X Ray ◽  
Adaptive Total Variation

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 Speckle Noise Removal by Energy Models with New Regularization Setting

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Bo Chen ◽  
Jinbin Zou ◽  
Weiqiang Zhang
Keyword(s):  
Total Variation ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Noise Removal ◽  
Energy Models ◽  
Energy Diffusion ◽  
Total Variation Model ◽  
Local Coordinates ◽  
Diffusion Speed ◽  
Adaptive Total Variation

In this paper, we introduce two novel total variation models to deal with speckle noise in ultrasound image in order to retain the fine details more effectively and to improve the speed of energy diffusion during the process. Firstly, two new convex functions are introduced as regularization term in the adaptive total variation model, and then, the diffusion performances of Hypersurface Total Variation (HYPTV) model and Logarithmic Total Variation (LOGTV) model are analyzed mathematically through the physical characteristics of local coordinates. We have shown that the larger positive parameter in the model is set, the greater energy diffusion speed appears to be, but it will cause the image to be too smooth that required adequate attention. Numerical experimental results show that our proposed LOGTV model for speckle noise removal is superior to traditional models, not only in visual effect but also in quantitative measures.

scholarly journals Optoacoustic model-based inversion using anisotropic adaptive total-variation regularization

2019 ◽  
Vol 16 ◽  
pp. 100142
Author(s):  
Shai Biton ◽  
Nadav Arbel ◽  
Gilad Drozdov ◽  
Guy Gilboa ◽  
Amir Rosenthal
Keyword(s):  
Total Variation ◽  
Total Variation Regularization ◽  
Model Based ◽  
Adaptive Total Variation
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