3D robust anisotropic diffusion filtering algorithm for sparse view neutron computed tomography 3D image reconstruction

The most critical part of a neutron computed tomography(NCT) system is the image processing algorithm,which directly affects the quality and speed of the reconstructed images.Various types of noise in the system can degrade the quality of the reconstructed images.Therefore,to improve the quality of the reconstructed images of NCT systems,efficient image processing algorithms must be used.The anisotropic diffusion filtering(ADF) algorithm can not only effectively suppress the noise in the projection data,but also preserve the image edge structure information by reducing the diffusion at the image edges.Therefore,we propose the application of the ADF algorithm for NCT image reconstruction.To compare the performance of different algorithms in NCT systems,we reconstructed images using the ordered subset simultaneous algebraic reconstruction technique(OS-SART) algorithm with different regular terms as image processing algorithms.In the iterative reconstruction,we selected two image processing algorithms,the Total Variation and split Bregman solved total variation algorithms,for comparison with the performance of the ADF algorithm.Additionally,the filtered back-projection algorithm was used for comparison with an iterative algorithm.By reconstructing the projection data of the numerical and clock models,we compared and analyzed the effects of each algorithm applied in the NCT system.Based on the reconstruction results,OS-SART-ADF outperformed the other algorithms in terms of denoising,preserving the edge structure,and suppressing artifacts.For example,when the 3D Shepp–Logan was reconstructed at 25 views,the root mean square error of OS-SART-ADF was the smallest among the four iterative algorithms,at only 0.0292.The universal quality index,mean structural similarity,and correlation coefficient of the reconstructed image were the largest among all algorithms,with values of 0.9877,0.9878,and 0.9887,respectively.

Anisotropic diffusion filtering for ultrasound speckle reduction

Speckle noise has long been known as a limiting factor for the quality of an ultrasound B-mode image.In this study,anisotropic diffusion filtering is proposed as an effective method for ultrasound speckle reduction.This article provides a brief description of anisotropic diffusion filtering proposed by Perona and Malik,and compares its speckle filtering effects with other filtering methods including median,moving average,and frequency domain Gaussian low-pass.In this study,multiple filters are implemented in Matlab.For each filter,three different types of noisy images with speckle noise are tested.The results show that anisotropic filter can reduce the noise more effectively and meanwhile preserve the boundaries of the objects.In addition,this filter has more controllable filtering parameters and is independent on the information of the noise.

ESPI filtering method based on anisotropic coherence diffusion and Perona-Malik diffusion

Noise reduction is one of the most important concerns in electronic speckle pattern interferometry(ESPI). According to partial differential equation(PDE) filtering theory, we present an anisotropic PDE noisereduction model based on fringe structure information for interferometric fringe patterns. This model is based on coherence diffusion and Perona-Malik(P-M) diffusion. The former can protect the structure information of fringe pattern, while the latter can effectively filter off the noise inside the fringes. The proposed model generated by the two diffusion methods helps to obtain good effects of denoising and fidelity. ESPI fringes and the phase pattern are tested. Experimental results validate the performance of the proposed filtering model.

Feature-Preserving Mesh Denoising via Anisotropic Surface Fitting

We propose in this paper a robust surface mesh denoising method that can effectively remove mesh noise while faithfully preserving sharp features. This method utilizes surface fitting and projection techniques. Sharp features are preserved in the surface fitting algorithm by considering an anisotropic neighborhood of each vertex detected by the normal-weighted distance. In addition, to handle the mesh with a high level of noise, we perform a pre-filtering of surface normals prior to the neighborhood searching. A number of experimental results and comparisons demonstrate the excellent performance of our method in preserving important surface geometries while filtering mesh noise.