Speckle Noise Filtering Algorithm by Using Wavelet Based on PCNN

2013 ◽  
Vol 10 (8) ◽  
pp. 2237-2246 ◽  
Author(s):  
Jian Xu
Keyword(s):  
Speckle Noise ◽  
Noise Filtering ◽  
Filtering Algorithm

scholarly journals Pixelated Carrier Phase-Shifting Shearography Using Spatiotemporal Low-Pass Filtering Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5185
Author(s):  
Peizheng Yan ◽  
Xiangwei Liu ◽  
Shuangle Wu ◽  
Fangyuan Sun ◽  
Qihan Zhao ◽  
...  
Keyword(s):  
Carrier Phase ◽  
Speckle Noise ◽  
Phase Shifting ◽  
Destructive Testing ◽  
Full Field ◽  
Filtering Algorithm ◽  
Real Time Measurement ◽  
Low Pass ◽  
Carrier Phase Shifting ◽  
Non Destructive

Shearography has been widely used in non-destructive testing due to its advantages in providing full-field, high precision, real-time measurement. The study presents a pixelated carrier phase-shifting shearography using a pixelated micropolarizer array. Based on the shearography, a series of shearograms are captured and phase maps corresponding to deformation are measured dynamically and continuously. Using the proposed spatiotemporal filtering algorithm in the complex domain, the set of phase maps are simultaneously low-pass filtered in the spatial and temporal domains, resulting in better phase quality than spatial low-pass filtering. By accumulating the temporally adjacent phase, the phase corresponding to large deformation can be evaluated; thus, large deformations can be accurately measured and protected from speckle noise, allowing internal defects to be easily identified. The capability of the proposed shearography is described by theoretical discussions and experiments.

scholarly journals Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information

2022 ◽  
Vol 14 (2) ◽  
pp. 367
Author(s):  
Zhen Zheng ◽  
Bingting Zha ◽  
Yu Zhou ◽  
Jinbo Huang ◽  
Youshi Xuchen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Single Stage ◽  
Estimation Methods ◽  
Computational Time ◽  
Noise Filtering ◽  
Filtering Algorithm ◽  
Multi Scale ◽  
Feature Information

This paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise filtering algorithm has difficulty quickly completing the single-stage adaptive filtering of multi-scale noise. The feature information from each point of the point cloud is obtained based on the efficient k-dimensional (k-d) tree data structure and amended normal vector estimation methods, and the adaptive threshold is used to divide the point cloud into large-scale noise, a feature-rich region, and a flat region to reduce the computational time. The large-scale noise is removed directly, the feature-rich and flat regions are filtered via improved bilateral filtering algorithm and weighted average filtering algorithm based on grey relational analysis, respectively. Simulation results show that the proposed algorithm performs better than the state-of-art comparison algorithms. It was, thus, verified that the algorithm proposed in this paper can quickly and adaptively (i) filter out large-scale noise, (ii) smooth small-scale noise, and (iii) effectively maintain the geometric features of the point cloud. The developed algorithm provides research thought for filtering pre-processing methods applicable in 3D measurements, remote sensing, and target recognition based on point clouds.

Speckle Noise Filtering for Sea SAR Image

2009 ◽  
Author(s):  
Yongxin Jiang ◽  
Xiaotong Wang ◽  
Xiaogang Xu ◽  
Xiyong Ye
Keyword(s):  
Speckle Noise ◽  
Noise Filtering ◽  
Sar Image

A SELF-DUAL FILTERING TOGGLE OPERATOR FOR SPECKLE NOISE FILTERING

2013 ◽  
Vol 13 (03) ◽  
pp. 1350015 ◽  
Author(s):  
LEYZA BALDO DORINI ◽  
NEUCIMAR JERÔNIMO LEITE
Keyword(s):  
Image Processing ◽  
Real World ◽  
Anisotropic Diffusion ◽  
Experimental Tests ◽  
Speckle Noise ◽  
Noise Filtering ◽  
Morphological Filters ◽  
Edge Information ◽  
Diffusion Technique ◽  
Homogeneous Regions

In this work, we formalize an alternative way to build self-dual morphological filters that extends some results obtained for morphological centers to a different class of toggle operators. Thus, a wider range of primitives can be considered without causing oscillations, a common problem in toggle mappings. We also show that the combination of the morphological filters generated by using the proposed approach with the well-known anisotropic diffusion technique yields sound results where homogeneous regions are smoothed without degrading edge information. We explore the filtering of speckle noise, an interference pattern that causes a granular aspect in the image, thus limiting its interpretation and making it difficult further image processing tasks. Experimental tests on both synthetic and real-world images show promising results when compared to some well-known methods related to this type of filtering.

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