scholarly journals Edge-Preserving Image Smoothing Via a Total Variation Regularizer and a Nonconvex Regularizer

2019 ◽  
Vol 154 ◽  
pp. 603-609 ◽  
Author(s):  
Li Jiang ◽  
Yu Han ◽  
Bin Xie
Keyword(s):  
Total Variation ◽  
Image Smoothing ◽  
Edge Preserving

scholarly journals A Soft-Threshold Filtering Approach for Tomography Reconstruction from a Limited Number of Projections with Bilateral Edge Preservation

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2346
Author(s):  
Tiago Wirtti ◽  
Evandro Salles
Keyword(s):  
Objective Function ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Edge Preservation ◽  
Edge Preserving ◽  
Reconstruction Process ◽  
Regularization Term ◽  
Computational Performance

In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with l 2 norm for fidelity function and some regularization function with l p norm, 1 < p < 2 . Among them stands out, both for its results and the computational performance, a technique that involves the alternating minimization of an objective function with l 2 norm for fidelity and a regularization term that uses discrete gradient transform (DGT) sparse transformation minimized by total variation (TV). This work proposes an improvement to the reconstruction process by adding a bilateral edge-preserving (BEP) regularization term to the objective function. BEP is a noise reduction method and has the purpose of adaptively eliminating noise in the initial phase of reconstruction. The addition of BEP improves optimization of the fidelity term and, as a consequence, improves the result of DGT minimization by total variation. For reconstructions with a limited number of projections (low-dose reconstruction), the proposed method can achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) results because it can better control the noise in the initial processing phase.

Real-Time Bilateral Filtering Using GPGPU

2015 ◽  
Vol 781 ◽  
pp. 568-571 ◽  
Author(s):  
Sanun Srisuk ◽  
Wachirapong Kesjindatanawaj ◽  
Surachai Ongkittikul
Keyword(s):  
Nonlinear Filtering ◽  
Computation Time ◽  
General Purpose ◽  
Experimental Results ◽  
Bilateral Filtering ◽  
Image Smoothing ◽  
Edge Preserving ◽  
Filter Kernel ◽  
Sharp Edges ◽  
General Purpose Gpu

In this paper, we present a technique for accelerating the bilateral filtering using GPGPU. Bilateral filtering is a tool for an image smoothing with edge preserving properties. It serves as a mixture of domain and range filters. Domain filter suppresses Gaussian noise while range filter maintains sharp edges. Bilateral filtering is a nonlinear filtering in which the filter kernel must be computed pixel by pixel. Therefore conventional fast Fourier transform technique cannot be used to accelerate the bilateral filtering. Instead, general purpose GPU is used as a parallel machine to reduce time consuming of the bilateral filtering. We will show the experimental results by comparing the computation time of CPU and GPU. It was cleared that, from the experimental results, GPU outperformed the CPU in terms of computation time.

scholarly journals Low-dose CT reconstruction via edge-preserving total variation regularization

2011 ◽  
Vol 56 (18) ◽  
pp. 5949-5967 ◽  
Author(s):  
Zhen Tian ◽  
Xun Jia ◽  
Kehong Yuan ◽  
Tinsu Pan ◽  
Steve B Jiang
Keyword(s):  
Total Variation ◽  
Low Dose ◽  
Total Variation Regularization ◽  
Ct Reconstruction ◽  
Edge Preserving ◽  
Low Dose Ct
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