The spline filter: A regularization approach for the Gaussian filter

2012 ◽  
Vol 36 (4) ◽  
pp. 586-592 ◽  
Author(s):  
Hao Zhang ◽  
Yibao Yuan ◽  
Weiying Piao
Mechanik ◽  
2017 ◽  
Vol 90 (3) ◽  
pp. 224-228 ◽  
Author(s):  
Aneta Łętocha

Paper presents the results of research in order to determine the impact of selected new filtration methods on roughness results of standard surfaces – sinusoidal (C type) and random (D type). Three filtering methods were chosen: robust Gaussian filter, spline filter, morphological filters. Studies with use contact method and confocal profiling method (optical) were made. Results of selected height roughness profile and surface parameters were analysed.


2015 ◽  
Vol 637 ◽  
pp. 57-68 ◽  
Author(s):  
Tatiana Miller ◽  
Aneta Łętocha ◽  
Krzysztof Gajda

The measurements were performed on the surfaces made of different materials and typical of diversified character. Glass roughness standards with sinusoidal profile, approximately sinusoidal profile and metallic comparative standards after lathing and grinding were object of research. Analysis was performed including the surface and profile evaluation. Statistical analysis was conducted. Measurement sections and other filter parameters were selected in accordance with standards. Measurements were carried out with stylus tip contact method – using TOPO 01P device designed by The Institute of Advanced Manufacturing Technology, that uses diamond tip inductive sensor. Tip sensor radius is equal to 2 μm. The results of measurements were filtered by: Gaussian filter, Robust Gaussian regression Filter, Spline, Spline Wavelet, Morphological Filter. Gaussian Filter uses linear system based on Fourier wavelengths. Robust Gauss Regression Filter is similar to Gaussian Filter, but it is insensitive on the specified phenomena in input signal. Spline Filter is based on linear polynomial combination. Wavelet Filter decomposes profile on constant shape elements, but on different scales. Morphological Filter operates on the principle of filtered profile plotting using circular disc or horizontal line segment with a specified (respectively) radius or length. Selection of suitable filtration method is essential and one of the most important things to obtain reliable measurement results evaluation. Not all filters are suitable for each type of surface. Filter algorithms differ from each other and this influences in a greater or lesser degree on the roughness profile and hence on roughness parameters and waviness parameters related to it.


2021 ◽  
Vol 179 ◽  
pp. 108057
Author(s):  
Zhao Zhang ◽  
Sheng Zhang ◽  
Jiashu Zhang
Keyword(s):  

2021 ◽  
Vol 11 (10) ◽  
pp. 4524
Author(s):  
Victor Getmanov ◽  
Vladislav Chinkin ◽  
Roman Sidorov ◽  
Alexei Gvishiani ◽  
Mikhail Dobrovolsky ◽  
...  

Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Junichi Tsuchiya ◽  
Kota Yokoyama ◽  
Ken Yamagiwa ◽  
Ryosuke Watanabe ◽  
Koichiro Kimura ◽  
...  

Abstract Background Deep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images obtained with the DL method with those obtained using a Gaussian filter. Methods Fifty patients with a mean age of 64.4 (range, 19–88) years who underwent 18F-FDG PET/CT between April 2019 and May 2019 were included in the study. PET images were obtained with the DL method in addition to conventional images reconstructed with three-dimensional time of flight-ordered subset expectation maximization and filtered with a Gaussian filter as a baseline for comparison. The reconstructed images were reviewed by two nuclear medicine physicians and scored from 1 (poor) to 5 (excellent) for tumor delineation, overall image quality, and image noise. For the semi-quantitative analysis, standardized uptake values in tumors and healthy tissues were compared between images obtained using the DL method and those obtained with a Gaussian filter. Results Images acquired using the DL method scored significantly higher for tumor delineation, overall image quality, and image noise compared to baseline (P < 0.001). The Fleiss’ kappa value for overall inter-reader agreement was 0.78. The standardized uptake values in tumor obtained by DL were significantly higher than those acquired using a Gaussian filter (P < 0.001). Conclusions Deep learning method improves the quality of PET images.


Sign in / Sign up

Export Citation Format

Share Document