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Статті в журналах з теми "GUIDED FILTERS"

Автор: Grafiati

Опубліковано: 26 жовтня 2023

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1

Vizilter, Y. V., A. Y. Rubis, and S. Y. Zheltov. "CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 403–10. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-403-2017.

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Анотація:

Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

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2

Toet, Alexander. "Alternating guided image filtering." PeerJ Computer Science 2 (June 27, 2016): e72. http://dx.doi.org/10.7717/peerj-cs.72.

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Edge preserving filters aim to simplify the representation of images (e.g., by reducing noise or eliminating irrelevant detail) while preserving their most significant edges. These filters are typically nonlinear and locally smooth the image structure while minimizing both blurring and over-sharpening of visually important edges. Here we present the Alternating Guided Filter (AGF) that achieves edge preserving smoothing by combining two recently introduced filters: the Rolling Guided Filter (RGF) and the Smooth and iteratively Restore Filter (SiR). We show that the integration of RGF and SiR in an alternating iterative framework results in a new smoothing operator that preserves significant image edges while effectively eliminating small scale details. The AGF combines the large scale edge and local intensity preserving properties of the RGF with the edge restoring properties of the SiR while eliminating the drawbacks of both previous methods (i.e., edge curvature smoothing by RGF and local intensity reduction and restoration of small scale details near large scale edges by SiR). The AGF is simple to implement and efficient, and produces high-quality results. We demonstrate the effectiveness of AGF on a variety of images, and provide a public code to facilitate future studies.

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3

Wu, Wenze, Leonard Weber, Finn-Niclas Stapelfeldt, Peter Hinze, Thomas Weimann, Bernd Bodermann, Stefanie Kroker, Joan Daniel Prades, Hutomo Suryo Wasisto, and Andreas Waag. "Pixel-Wise Multispectral Sensing System Using Nanostructured Filter Matrix for Biomedical Applications." Proceedings 2, no. 13 (November 29, 2018): 880. http://dx.doi.org/10.3390/proceedings2130880.

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In this work, a novel multispectral sensing system consisting of nanostructured filter matrix and a charge-coupled device (CCD)-based image sensor has been developed to overcome the limitation of the conventional pigment filtered sensors, which are difficult to be fabricated at a microscale and usually showing a pronounced degradation. By designing the filters in guided-mode resonance (GMR) architecture, light transmission efficiencies of ~90% with low sidebands and sharp peaks can be obtained, which are critical characteristics for realizing precise optical measurement systems. To optimize the transmission functions, various materials and structural parameters have been simulated. Electron beam nanolithography is employed in the device fabrication to fabricate pixel-wise independent filter functions. After being characterized in terms of their wavelength filtering capability, the developed GMR filters are then combined with image sensors, particularly for addressing biological applications.

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4

Roth, Zachary A., Pradeep Srinivasan, Menelaos K. Poutous, Aaron J. Pung, Raymond C. Rumpf, and Eric G. Johnson. "Azimuthally Varying Guided Mode Resonance Filters." Micromachines 3, no. 1 (March 15, 2012): 180–93. http://dx.doi.org/10.3390/mi3010180.

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5

Magnusson, R., and S. S. Wang. "Transmission bandpass guided-mode resonance filters." Applied Optics 34, no. 35 (December 10, 1995): 8106. http://dx.doi.org/10.1364/ao.34.008106.

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6

Mousa, Mohamed A., Nadia H. Rafat, and Amr A. E. Saleh. "Toward spectrometerless instant Raman identification with tailored metasurfaces-powered guided-mode resonances (GMR) filters." Nanophotonics 10, no. 18 (October 20, 2021): 4567–77. http://dx.doi.org/10.1515/nanoph-2021-0444.

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Анотація:

Abstract Raman identification is an instrumental tool with a broad range of applications, yet current spectroscopy approaches fall short in facilitating practical and scalable Raman identification platforms. In this work, we introduce a spectrometerless Raman identification approach that utilizes guided-mode resonance filters. Unlike arrayed narrowband-filters spectrometer, we tailor the transmission characteristics of each filter to match the Raman signature of a given target. Hence, instantaneous Raman identification could be directly achieved at the hardware level with no spectral data post-processing. The filters consist of a metasurface grating encapsulated between two identical distributed Bragg reflectors and are characterized by transmission peaks line-widths narrower than 0.01 nm and transmission efficiency exceeding 98%. We develop a rigorous design methodology to customize the filters’ characteristics such that the maximum optical transmission through a given filter is only attained when exposed to the Raman scattering from its matched target. To illustrate the potential of our approach, we theoretically investigate the identification of four different saccharides as well as the classification of two antibiotic-susceptible and resistant strains of Staphylococcus aureus. We show that our proposed approach can accurately identify these targets. Our work lays the foundation for a new-generation of scalable, compact, and cost-effective instant Raman identification platforms that can be adopted in countless applications from wearables and point-of-care diagnostics to in-line quality control in food and pharmaceutical industries.

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7

Uddin, Mohammad Jalal, and Robert Magnusson. "Efficient Guided-Mode-Resonant Tunable Color Filters." IEEE Photonics Technology Letters 24, no. 17 (September 2012): 1552–54. http://dx.doi.org/10.1109/lpt.2012.2208453.

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8

Vallius, Tuomas, Pasi Vahimaa, and Jari Turunen. "Pulse deformations at guided-mode resonance filters." Optics Express 10, no. 16 (August 12, 2002): 840. http://dx.doi.org/10.1364/oe.10.000840.

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9

Liu, Wenxing, Zhenquan Lai, Hao Guo, and Ying Liu. "Guided-mode resonance filters with shallow grating." Optics Letters 35, no. 6 (March 15, 2010): 865. http://dx.doi.org/10.1364/ol.35.000865.

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10

Cannistra, Aaron T., Menelaos K. Poutous, Eric G. Johnson, and Thomas J. Suleski. "Performance of conformal guided mode resonance filters." Optics Letters 36, no. 7 (March 25, 2011): 1155. http://dx.doi.org/10.1364/ol.36.001155.

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11

Rochon, P., A. Natansohn, C. L. Callender, and L. Robitaille. "Guided mode resonance filters using polymer films." Applied Physics Letters 71, no. 8 (August 25, 1997): 1008–10. http://dx.doi.org/10.1063/1.119710.

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12

Boonruang, Sakoolkan, Andrew Greenwell, and M. G. Moharam. "Multiline two-dimensional guided-mode resonant filters." Applied Optics 45, no. 22 (August 1, 2006): 5740. http://dx.doi.org/10.1364/ao.45.005740.

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13

Le Touze, G., B. Nicolas, J. I. Mars, and J. L. Lacoume. "Matched Representations and Filters for Guided Waves." IEEE Transactions on Signal Processing 57, no. 5 (May 2009): 1783–95. http://dx.doi.org/10.1109/tsp.2009.2013907.

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14

Hao, Shijie, Daru Pan, Yanrong Guo, Richang Hong, and Meng Wang. "Image detail enhancement with spatially guided filters." Signal Processing 120 (March 2016): 789–96. http://dx.doi.org/10.1016/j.sigpro.2015.02.017.

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15

Uddin, Mohammad Jalal, and Robert Magnusson. "Guided-Mode Resonant Thermo-Optic Tunable Filters." IEEE Photonics Technology Letters 25, no. 15 (August 2013): 1412–15. http://dx.doi.org/10.1109/lpt.2013.2266272.

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16

Tibuleac, S., and R. Magnusson. "Reflection and transmission guided-mode resonance filters." Journal of the Optical Society of America A 14, no. 7 (July 1, 1997): 1617. http://dx.doi.org/10.1364/josaa.14.001617.

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17

Xiong, Biao, Qiegen Liu, Jiaojiao Xiong, Sanqian Li, Shanshan Wang, and Dong Liang. "Field-of-Experts Filters Guided Tensor Completion." IEEE Transactions on Multimedia 20, no. 9 (September 2018): 2316–29. http://dx.doi.org/10.1109/tmm.2018.2806225.

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18

Saarinen, Jyrki. "Guided-mode resonance filters of finite aperture." Optical Engineering 34, no. 9 (September 1, 1995): 2560. http://dx.doi.org/10.1117/12.208079.

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19

Dash, Sonali, Sahil Verma, Kavita, Savitri Bevinakoppa, Marcin Wozniak, Jana Shafi, and Muhammad Fazal Ijaz. "Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction." Symmetry 14, no. 2 (January 19, 2022): 194. http://dx.doi.org/10.3390/sym14020194.

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Анотація:

Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

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20

Gautam, Divya, Kavita Khare, and Bhavana P. Shrivastava. "A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising." Applied Sciences 13, no. 12 (June 11, 2023): 7032. http://dx.doi.org/10.3390/app13127032.

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Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.

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21

Salehi, Hadi, Javad Vahidi, Thabet Abdeljawad, Aziz Khan, and Seyed Yaser Bozorgi Rad. "A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter." Remote Sensing 12, no. 15 (July 23, 2020): 2371. http://dx.doi.org/10.3390/rs12152371.

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The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

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22

Rayner, Justine, Brian Skinner, and Daniele Lantagne. "Current practices in manufacturing locally-made ceramic pot filters for water treatment in developing countries." Journal of Water, Sanitation and Hygiene for Development 3, no. 2 (June 1, 2013): 252–61. http://dx.doi.org/10.2166/washdev.2013.178.

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Locally produced ceramic pot filters have been shown to improve the microbiological quality of household drinking water and reduce the burden of diarrheal disease in users. They are considered one of the most promising household water treatment methods. However, overarching manufacturing and quality control guidelines do not exist for the 35 decentralized filter factories in 18 countries that currently produce filters. In this study, we conducted manufacturing process surveys with 25 filter factories worldwide to document production methods and identify areas where manufacturing and quality control guidelines are needed. Our results show that manufacturing processes vary widely both between and within factories, including the consistency of materials, manufacturing methods, and quality control practices. These variations pose concerns about the consistency and quality of locally produced filters in the absence of standardized quality control procedures. We propose areas where manufacturing guidelines are needed to assist factories in producing consistently high quality filters, and identify topics where further research is needed to refine manufacturing recommendations. These results guided the development of a best practice manual that described consensus-based recommendations to advance consistent, quality-controlled filter production world-wide.

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23

Wang, S. S., and R. Magnusson. "Theory and applications of guided-mode resonance filters." Applied Optics 32, no. 14 (May 10, 1993): 2606. http://dx.doi.org/10.1364/ao.32.002606.

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24

Saleem, M. R., P. Stenberg, T. Alasaarela, P. Silfsten, M. B. Khan, S. Honkanen, and J. Turunen. "Towards athermal organic-inorganic guided mode resonance filters." Optics Express 19, no. 24 (November 14, 2011): 24241. http://dx.doi.org/10.1364/oe.19.024241.

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25

Wang, Zhanshan, Tian Sang, Li Wang, Jingtao Zhu, Yonggang Wu, and Lingyan Chen. "Guided-mode resonance Brewster filters with multiple channels." Applied Physics Letters 88, no. 25 (June 19, 2006): 251115. http://dx.doi.org/10.1063/1.2215610.

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26

Uddin, Mohammad Jalal, Tanzina Khaleque, and Robert Magnusson. "Guided-mode resonant polarization-controlled tunable color filters." Optics Express 22, no. 10 (May 13, 2014): 12307. http://dx.doi.org/10.1364/oe.22.012307.

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27

Kodali, Anil K., Matthew Schulmerich, Jason Ip, Gary Yen, Brian T. Cunningham, and Rohit Bhargava. "Narrowband Midinfrared Reflectance Filters Using Guided Mode Resonance." Analytical Chemistry 82, no. 13 (July 2010): 5697–706. http://dx.doi.org/10.1021/ac1007128.

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28

Wu, Mount-Learn, Che-Lung Hsu, Hsiao-Chin Lan, Hung-I. Huang, Yung-Chih Liu, Zong-Ru Tu, Chien-Chieh Lee, Jian-Shian Lin, Chih-Chieh Su, and Jenq-Yang Chang. "Authentication labels based on guided-mode resonant filters." Optics Letters 32, no. 12 (June 5, 2007): 1614. http://dx.doi.org/10.1364/ol.32.001614.

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29

Bao, G., and K. Huang. "Optimal Design of Guided-Mode Grating Resonance Filters." IEEE Photonics Technology Letters 16, no. 1 (January 2004): 141–43. http://dx.doi.org/10.1109/lpt.2003.818927.

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30

Wei, Chaoyang, Shijie Liu, Degang Deng, Jian Shen, Jianda Shao, and Zhengxiu Fan. "Electric field enhancement in guided-mode resonance filters." Optics Letters 31, no. 9 (May 1, 2006): 1223. http://dx.doi.org/10.1364/ol.31.001223.

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31

Oberberger, Max, Matthäus G. Chajdas, and Rüdiger Westermann. "Spatiotemporal Variance-Guided Filtering for Motion Blur." Proceedings of the ACM on Computer Graphics and Interactive Techniques 5, no. 3 (July 25, 2022): 1–13. http://dx.doi.org/10.1145/3543871.

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Adding motion blur to a scene can help to convey the feeling of speed even at low frame rates. Monte Carlo ray tracing can compute accurate motion blur, but requires a large number of samples per pixel to converge. In comparison, rasterization, in combination with a post-processing filter, can generate fast, but not accurate motion blur from a single sample per pixel. We build upon a recent path tracing denoiser and propose its variant to simulate ray-traced motion blur, enabling fast and high-quality motion blur from a single sample per pixel. Our approach creates temporally coherent renderings by estimating the motion direction and variance locally, and using these estimates to guide wavelet filters at different scales. We compare image quality against brute force Monte Carlo methods and current post-processing motion blur. Our approach achieves real-time frame rates, requiring less than 4ms for full-screen motion blur at a resolution of 1920 x 1080 on recent graphics cards.

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32

Prasad, K. D., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Image by Median Filtering over Wiener Filtering." CARDIOMETRY, no. 25 (February 14, 2023): 996–1002. http://dx.doi.org/10.18137/cardiometry.2022.25.9961002.

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Aim: The purpose of this study is to use median filters and wiener filters to minimize noise in liver images in order to improve them. In addition, the output of both filters was analyzed based on their Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. Image enhancement is used to enhance the niceness of a picture for the visible notion of human beings. The kaggle website was used to collect data for this study. According to clinical.com, samples were considered as size 20 for PSNR ratio of image G power of 80%, and total sample size determined. Using matlab programming and a standard data set, the Linear filtering, Median filtering were computed. Results: According to Matlab simulation results, unique median filters have a PSNR of 48.1240, while wiener filters have a PSNR of 67.8360. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical insignificant difference between both techniques. The significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05 was found in the statistical analysis. Conclusion: On ultrasound liver pictures, the innovative median filter gives greater PSNR than the wiener filter for medical image enhancing purposes, according to this study.

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33

Prasad, K. D., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Images by Linear Contrast Algorithm over Median Filtering." CARDIOMETRY, no. 25 (February 14, 2023): 983–89. http://dx.doi.org/10.18137/cardiometry.2022.25.983989.

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Анотація:

Aim: The goal of this research is to reduce noise present in the liver images in order to enhance it using linear contrast and median filters. And also to analyze output of both the filters based on its Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. For this study, the affected and normal liver images were collected from the Kaggle website. Then the linear filtering, median filtering algorithms were executed using Matlab software. Sample size was calculated using clinicalc.com, and the comparison analysis has been carried out through SPSS software. This research contains two groups, with a Gpower of 80 percent. The performance of the novel median filter is evaluated and the performance measure PSNR is compared with the linear contrast filter. Result: Based on Matlab simulation results, the PSNR of novel median filters is 76.0355 and linear contrast filters have PSNR of 57.1785. From the statistical analysis, it is observed that the significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p>0.05. Conclusion: This study reveals that for the medical image enhancement purpose the novel median filter provides better PSNR than the linear contrast algorithm on ultrasound liver images.

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34

Amărioarei, Alexandru, Frankie Spencer, Gefry Barad, Ana-Maria Gheorghe, Corina Iţcuş, Iris Tuşa, Ana-Maria Prelipcean, et al. "DNA-Guided Assembly for Fibril Proteins." Mathematics 9, no. 4 (February 19, 2021): 404. http://dx.doi.org/10.3390/math9040404.

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Анотація:

Current advances in computational modelling and simulation have led to the inclusion of computer scientists as partners in the process of engineering of new nanomaterials and nanodevices. This trend is now, more than ever, visible in the field of deoxyribonucleic acid (DNA)-based nanotechnology, as DNA’s intrinsic principle of self-assembly has been proven to be highly algorithmic and programmable. As a raw material, DNA is a rather unremarkable fabric. However, as a way to achieve patterns, dynamic behavior, or nano-shape reconstruction, DNA has been proven to be one of the most functional nanomaterials. It would thus be of great potential to pair up DNA’s highly functional assembly characteristics with the mechanic properties of other well-known bio-nanomaterials, such as graphene, cellulos, or fibroin. In the current study, we perform projections regarding the structural properties of a fibril mesh (or filter) for which assembly would be guided by the controlled aggregation of DNA scaffold subunits. The formation of such a 2D fibril mesh structure is ensured by the mechanistic assembly properties borrowed from the DNA assembly apparatus. For generating inexpensive pre-experimental assessments regarding the efficiency of various assembly strategies, we introduced in this study a computational model for the simulation of fibril mesh assembly dynamical systems. Our approach was based on providing solutions towards two main circumstances. First, we created a functional computational model that is restrictive enough to be able to numerically simulate the controlled aggregation of up to 1000s of elementary fibril elements yet rich enough to provide actionable insides on the structural characteristics for the generated assembly. Second, we used the provided numerical model in order to generate projections regarding effective ways of manipulating one of the the key structural properties of such generated filters, namely the average size of the openings (gaps) within these meshes, also known as the filter’s aperture. This work is a continuation of Amarioarei et al., 2018, where a preliminary version of this research was discussed.

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35

Parker, Kevin J. "Limitations of Curl and Directional Filters in Elastography." Acoustics 5, no. 2 (June 16, 2023): 575–85. http://dx.doi.org/10.3390/acoustics5020035.

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In the approaches to elastography, two mathematical operations have been frequently applied to improve the final estimate of shear wave speed and shear modulus of tissues. The vector curl operator can separate out the transverse component of a complicated displacement field, and directional filters can separate distinct orientations of wave propagation. However, there are practical limitations that can prevent the intended improvement in elastography estimates. Some simple configurations of wavefields relevant to elastography are examined against theoretical models within the semi-infinite elastic medium and guided waves in a bounded medium. The Miller–Pursey solutions in simplified form are examined for the semi-infinite medium and the Lamb wave symmetric form is considered for the guided wave structure. In both cases, we examine simple but practical wave combinations that can prevent the curl and directional filter operations from directly providing an improved measure of shear wave speed and shear modulus. Additional factors including signal-to-noise and the support of filters also restrict the applicability of these strategies for improving elastographic measures. Thus, some implementations of shear wave excitations applied to the body and to bounded structures within the body are shown to involve waves that are not easily resolved by the vector curl operator and directional filters. These limitations may be overcome by more advanced strategies or simple improvements in baseline parameters including the size of the region of interest and the number of shear waves propagated within.

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36

Zhou, Yan. "Prediction and Analysis of Conduction Electromagnetic Interference in Communication Power." Journal of Nanoelectronics and Optoelectronics 16, no. 12 (December 1, 2021): 1892–96. http://dx.doi.org/10.1166/jno.2021.3155.

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Conducted electromagnetic interference (EMI) in power electronic equipment is an important factor restricting the development of power electronics technology, and the research is of great hot and difficulty. The prediction of conducted EMI is to effectively guide the design of EMI filters. On the one hand, the selection of commonmode capacitance and inductance values can be guided by the analysis of propagation path impedance; on the other hand, various passive devices are analyzed by simulation the influence of parasitic parameters on conducted EMI can guide the selection of filter capacitors and inductors. In this paper, we take the widely used switching power supply as an example, a novel EMI conduction modelling is proposed, the impedance analysis method is used to model the high-frequency parasitic parameters, the model was built for predicting the conducted EMI accurately of switching power supply.

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37

Ignotus, P., C. Wetton, and J. Berry. "CT fluoroscopic guided insertion of inferior vena cava filters." British Journal of Radiology 79, no. 939 (March 2006): 258–60. http://dx.doi.org/10.1259/bjr/43028256.

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38

Saleem, M. R., R. Ali, S. Honkanen, and J. Turunen. "Bio-molecular sensors based on guided mode resonance filters." IOP Conference Series: Materials Science and Engineering 146 (August 2016): 012030. http://dx.doi.org/10.1088/1757-899x/146/1/012030.

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39

Xue, Zhiguang, Houzhu Zhang, Yang Zhao, and Sergey Fomel. "Pattern‐guided dip estimation with plane‐wave destruction filters." Geophysical Prospecting 67, no. 7 (May 15, 2019): 1798–810. http://dx.doi.org/10.1111/1365-2478.12798.

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40

Calvez, Stephane, Antoine Monmayrant, and Olivier Gauthier-Lafaye. "Thermally-tunable cavity resonator-integrated guided-mode resonance filters." OSA Continuum 2, no. 11 (November 4, 2019): 3204. http://dx.doi.org/10.1364/osac.2.003204.

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41

Xu-Hui, Hu, Gong Ke, Sun Tian-Yu, and Wu Dong-Min. "Polarization-Independent Guided-Mode Resonance Filters under Oblique Incidence." Chinese Physics Letters 27, no. 7 (July 2010): 074211. http://dx.doi.org/10.1088/0256-307x/27/7/074211.

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42

Baadh, Amanjit S., Stephen Rivoli, Jack Ansell, and Robert E. Graham. "Indications for Inferior Vena Cava (IVC) Filter Placement - Assessing Compliance with Accepted Standards Set by Two Professional Societies." Blood 116, no. 21 (November 19, 2010): 2553. http://dx.doi.org/10.1182/blood.v116.21.2553.2553.

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Abstract Abstract 2553 Background: Inferior Vena Cava (IVC) filter placement has increased significantly over the past few decades, due to expanding indications for filter placement. Indications for filter placement vary widely depending on which professional society recommendations are followed. Our objectives were to record the number of IVC filters placed in our medium sized metropolitan teaching hospital, assess the effect of medical specialty on placement and evaluate compliance with accepted standards as set by the American College of Chest Physicians (ACCP) and the Society of Interventional Radiology (SIR). Methods: Single-center, retrospective medical record review of all patients who received an IVC filter over 26 months (01/30/2008 - 4/5/2010). Inclusion criteria included patients from both sexes, all ages, filter placement within the aforementioned dates and inpatient procedures performed by interventional radiology. A total of 443 IVC filters were placed in our institution over the time period studied. 48.1% (213) of these filters were placed by interventional radiology. Of these, 187 were reviewed with 26 excluded do to incomplete patient records available at the time of review (July 2010). Medical records were reviewed for patient demographics, clinical course, and compliance with accepted guidelines set by the ACCP and SIR. Results: The average age was 75.3 years and 43.9% of the patients were males. 76.2% of patients were on the medical service (internal medicine and its subspecialties) whereas 22.8% were on non medical services. 87.2 % of filters were recommended by medicine and its subspecialties and 12.8% by non medical specialties. 43.3% of filters placed met guidelines established by the ACCP (Table 1). 79.1% of filters placed met SIR guidelines (Table 2). No documentation was available to assess compliance for 20.9% of filters. 46% of filters placed by internal medicine and its subspecialties met ACCP criteria whereas only 25% of filters recommended by non medicine specialties were compliant with criteria (p value=0.039, 95% CI). Physicians within internal medicine and its subspecialties were compliant with SIR guidelines for 84% of the filters placed, whereas only 46% of non medicine physicians met these indications (p=0.001, 95% CI). 35.8% of filters placed met SIR criteria but did not meet ACCP guidelines. Conclusions: Indications for IVC filter placement varied significantly in this study, less than half of filters placed met ACCP guidelines, yet over three-fourths met criteria set by the SIR, especially when comparing medicine and non medicine specialties. In analyzing the filters which meet indications set by SIR but not ACCP it becomes apparent that most of these are placed for patients classified as “fall risks”, failures of anticoagulation, limited cardiopulmonary reserve and medication noncompliance. Further research needs to be guided towards evaluating if these indications truly merit the placement of an IVC filter. This study strongly suggests a need for harmonization of current guidelines espoused by professional societies. A limitation of our study was that 230 filters placed by vascular surgery and interventional cardiology were not reviewed. Disclosures: No relevant conflicts of interest to declare.

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43

Palau, Roberta de Carvalho Nobre, Wagner Penny, Ramiro Viana, Jones Goebel, Guilherme Correa, Marcelo Porto, and Luciano Agostini. "High-Throughput Hardware Design for the AV1 Decoder Switchable Loop Restoration Filters." Journal of Integrated Circuits and Systems 18, no. 1 (May 22, 2023): 1–12. http://dx.doi.org/10.29292/jics.v18i1.667.

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This paper presents a high-throughput hardware design for the Switchable Loop Restoration Filter (SLRF) of the AOM Video 1 (AV1) video format. This hardware includes the two filters defined at the AV1 SLRF: the Separable Symmetric Normalized Wiener Filter (SSNWF) and the Dual Self- Guided Filter (DSGF). The SLRF is the last step in the AV1 loop restoration filters, and it is used to attenuate blurring artifacts, improving the subjective video quality and the coding efficiency. The designed hardware targeted the AV1 decoderand is able to process up to 4K Ultra-High Definition (4K UHD) videos (with 3840x2160 pixels) at 60 frames per second (fps) in real-time. In order to cover different scenarios, two other target throughputs were also evaluated: 4K UHD at 30fps and Full HD (FHD) (with 1920x1080 pixels) at 30fps. The architectures were synthesized for standard cells using the 40 nm TSMC library. The SSNWF and DSGF architectures used 37.38 Kgates and 177.58 kgates in all evaluated scenarios. Depending on the evaluated scenario, the SSNWF power dissipation varied from 8.25mWto 26.95mWand the DSGF power varied from 57.19 mW to 115.02 mW. This is the first paper in the literature presenting a hardware design for the AV1 SLRF with its two filters.

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44

Dai, Junjie, Tianpeng Li, and Zhaolong Xuan. "Guided Filter-Based Edge Detection Algorithm for ICT Images of Solid Rocket Motor Propellant." Electronics 11, no. 14 (July 6, 2022): 2118. http://dx.doi.org/10.3390/electronics11142118.

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Анотація:

As the nondestructive testing method based on industrial computerized tomography (ICT) is widely used in solid rocket motor (SRM) propellant defect detection, the demand for a corresponding image processing algorithm is increasing. In order to extract better defect information on SRM propellants, we studied the edge detection algorithm for their ICT images. This paper proposes a guided filter-based edge detection algorithm for ICT images of SRM propellants with much noise. The algorithm innovatively uses guided filters to converge the detection results of type I edges with good edge continuity to type II edges with clear positioning. The obtained type III edges have good edge continuity and clear positioning. The experimental results show that the proposed algorithm can achieve edge detection effectively.

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45

Zhang, Liping, Xinran Wang, Xiaoli Dong, Linjun Sun, Weiwei Cai, and Xin Ning. "Finger Vein Image Enhancement Based on Guided Tri-Gaussian Filters." ASP Transactions on Pattern Recognition and Intelligent Systems 1, no. 1 (April 27, 2021): 17–23. http://dx.doi.org/10.52810/tpris.2021.100012.

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In the process of image acquisition, the contrast between veins and non-veins in finger vein images is not high due to the influence of the fuzzy light source, skin scattering and finger movement. To solve this problem, a finger vein image enhancement method is proposed (GTGFs), which enhances finger vein patterns by setting guided image as input image firstly. On this basis, the tri-Gaussian model is based on disinhibitory properties of the concentric receptive field used to locally enhancing the image. The parameters of the tri-Gaussian model are determined based on the finger vein width information. The experiment results show that the proposed enhancement method can significantly enhance the finger vein patterns and improve the recognition effect of the methods based on vein pattern segmentation.

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46

Zhang Dawei, 张大伟, 王琦 Wang Qi, 朱亦鸣 Zhu Yiming, 黄元申 Huang Yuanshen, 倪争技 Ni Zhengji, and 庄松林 Zhuang Songlin. "Design of Guided Mode Resonant Filters Tuned by Azimuthal Angle." Chinese Journal of Lasers 37, no. 4 (2010): 950–53. http://dx.doi.org/10.3788/cjl20103704.0950.

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47

Tuambilangana, Christelle, Fabrice Pardo, Emilie Sakat, Patrick Bouchon, Jean-Luc Pelouard, and Riad Haïdar. "Two-mode model for metal-dielectric guided-mode resonance filters." Optics Express 23, no. 25 (November 30, 2015): 31672. http://dx.doi.org/10.1364/oe.23.031672.

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48

Chiou, Andy C. "Intravascular Ultrasound-Guided Bedside Placement of Inferior Vena Cava Filters." Seminars in Vascular Surgery 19, no. 3 (September 2006): 150–54. http://dx.doi.org/10.1053/j.semvascsurg.2006.06.002.

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49

Qi, Yanjie, Michael Singh, Jason Kim, Ankur Chandra, Karl A. Illig, and David Gillespie. "PS152. Intravascular Ultrasound-Guided IVC Filters in the ICU Population." Journal of Vascular Surgery 53, no. 6 (June 2011): 69S—70S. http://dx.doi.org/10.1016/j.jvs.2011.03.133.

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50

Wang, Danyan, Qingkang Wang, and Jian Gao. "Spectral Correlation Between 2D and 1D Guided Mode Resonant Filters." IEEE Photonics Technology Letters 31, no. 15 (August 1, 2019): 1289–92. http://dx.doi.org/10.1109/lpt.2019.2925770.

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