A Total Variation Method for MR Renography Denoising based on Mathematical Morphology Reconstruction

Stone tablet is one of the important historical and cultural heritages of China. It is not only an important carrier of China ancient civilization, but also is a classical template to research and learn the art of calligraphy. Chinese calligraphy tablet documents that obtained through rubbing were featured by many fuzzy details, bad effect and so on, so it might lose more details in the traditional handling process. In this paper, a total variation (TV) based denoising pre-processing methods for rubbing image has been used, and then a segmentation algorithm based on mathematical morphology is applied. Experiments show this algorithm overcomes the shortcomings of traditional method and weakens the contradiction between the noise suppression and the accuracy of detecting edge details, so it has better practicality.

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Speckle reduction in optical coherence tomography by adaptive total variation method

Journal of Modern Optics ◽

10.1080/09500340.2015.1054444 ◽

2015 ◽

Vol 62 (21) ◽

pp. 1849-1855 ◽

Cited By ~ 1

Author(s):

Tong Wu ◽

Yaoyao Shi ◽

Youwen Liu ◽

Chongjun He

Keyword(s):

Optical Coherence Tomography ◽

Total Variation ◽

Variation Method ◽

Speckle Reduction ◽

Optical Coherence ◽

Adaptive Total Variation

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Enhanced Image Inpainting in Remotely Sensed Images by Optimizing NLTV model by Ant Colony Optimization

International Journal of Electrical and Electronics Research ◽

10.37391/ijeer.090306 ◽

2016 ◽

Vol 4 (3) ◽

pp. 91-97

Author(s):

Manjinder Singh ◽

Harpreet Kaur

Keyword(s):

Ant Colony Optimization ◽

Total Variation ◽

Remotely Sensed ◽

Ant Colony ◽

Variation Method ◽

Ant Colony Optimization Algorithm ◽

Edge Preserving ◽

Nonlocal Total Variation ◽

Remotely Sensed Images ◽

Variation Model

Filling dead pixels or eliminating unwanted things is typically preferred within the applications of remotely sensed images. In proposed article, a competent image imprinting technique is demonstrated to resolve this drawback, relied nonlocal total variation. Initially remotely sensed images are effected by ill posed inverse problems i.e. image destripping, image de-noising etc. So it is required to use regularization technique to makes these problems well posed i.e. NLTV method, which is the combination of nonlocal operators and total variation model. Actually this method can make use of the good features of non-local operators for textured images and total variation method in edge preserving for color images. To optimize the proposed variation model, an Ant Colony Optimization algorithm is used in order to get similarity with the original image. And evaluate the outcomes of proposed technique with the existing technique i.e. MNLTV optimized by Bregmanized-operator-splitting algorithm which is a prediction based method. The investigation of all outcomes confirms the efficacy of this rule.

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Reconstruction of Optical Coherence Tomography Images Using Mixed Low Rank Approximation and Second Order Tensor Based Total Variation Method

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2020.3040270 ◽

2020 ◽

pp. 1-1

Author(s):

Parisa Ghaderi Daneshmand ◽

Alireza Mehridehnavi ◽

Hossein Rabbani

Keyword(s):

Optical Coherence Tomography ◽

Total Variation ◽

Second Order ◽

Low Rank ◽

Variation Method ◽

Optical Coherence ◽

Low Rank Approximation ◽

Order Tensor ◽

Rank Approximation

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Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging

Remote Sensing ◽

10.3390/rs12182877 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2877

Author(s):

Xingyu Tuo ◽

Yin Zhang ◽

Yulin Huang ◽

Jianyu Yang

Keyword(s):

Total Variation ◽

Structural Characteristics ◽

Super Resolution ◽

Variation Method ◽

High Dimensional ◽

Imaging Method ◽

Lp Norm ◽

L2 Norm ◽

The Matrix ◽

Resolution Imaging

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation.

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