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Journal articles on the topic 'Prefiltering'

Author: Grafiati

Published: 3 June 2025

Last updated: 13 July 2025

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1

Tian-Tsong Ng and Shih-Fu Chang. "Identifying and prefiltering images." IEEE Signal Processing Magazine 26, no. 2 (2009): 49–58. http://dx.doi.org/10.1109/msp.2008.931077.

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2

Itoh, Katsuko, Tetsuya Shimamura, and Jouji Suzuki. "Prefiltering for blind equalization." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 78, no. 9 (1995): 1–11. http://dx.doi.org/10.1002/ecjc.4430780901.

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3

Kumaresan, R., and Y. Feng. "FIR prefiltering improves Prony's method." IEEE Transactions on Signal Processing 39, no. 3 (1991): 736–41. http://dx.doi.org/10.1109/78.80860.

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4

Hong, X., P. A. Wilson, and C. J. Harris. "Neurofuzzy state identification using prefiltering." IEE Proceedings - Control Theory and Applications 146, no. 2 (1999): 234–40. http://dx.doi.org/10.1049/ip-cta:19990121.

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5

Greenhalgh, P. A., R. D. Abel, and P. A. Davuies. "Optical prefiltering in subcarrier systems." Electronics Letters 28, no. 19 (1992): 1850. http://dx.doi.org/10.1049/el:19921180.

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6

Lindner, R. "A Prefiltering Raster Scan Algorithm." Computer Graphics Forum 4, no. 2 (1985): 101–10. http://dx.doi.org/10.1111/j.1467-8659.1985.tb00199.x.

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7

Ghayekhloo, Samira, and Zeki Bayram. "Prefiltering Strategy to Improve Performance of Semantic Web Service Discovery." Scientific Programming 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/576463.

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Discovery of semantic Web services is a heavyweight task when the number of Web services or the complexity of ontologies increases. In this paper, we present a new logical discovery framework based on semantic description of the capability of Web services and user goals using F-logic. Our framework tackles the scalability problem and improves discovery performance by adding two prefiltering stages to the discovery engine. The first stage is based on ontology comparison of user request and Web service categories. In the second stage, yet more Web services are eliminated based upon a decomposition and analysis of concept and instance attributes used in Web service capabilities and the requested capabilities of the client, resulting in a much smaller pool of Web services that need to be matched against the client request. Our prefiltering approach is evaluated using a new Web service repository, called WSMO-FL test collection. The recall rate of the filtering process is 100% by design, since no relevant Web services are ever eliminated by the two prefiltering stages, and experimental results show that the precision rate is more than 53%.

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8

Ramachandra, Vikas, Keigo Hirakawa, Matthias Zwicker, and Truong Nguyen. "Spatioangular Prefiltering for Multiview 3D Displays." IEEE Transactions on Visualization and Computer Graphics 17, no. 5 (2011): 642–54. http://dx.doi.org/10.1109/tvcg.2010.86.

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9

Vrhel, M. J., and A. Aldroubi. "Projection based prefiltering for multiwavelet transforms." IEEE Transactions on Signal Processing 46, no. 11 (1998): 3088–92. http://dx.doi.org/10.1109/78.726821.

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10

Hildebrand, R., A. Lecchini, G. Solari, and M. Gevers. "Optimal prefiltering in iterative feedback tuning." IEEE Transactions on Automatic Control 50, no. 8 (2005): 1196–200. http://dx.doi.org/10.1109/tac.2005.852554.

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11

D'Andrea, A., U. Mengali, and M. Moro. "Nearly Optimum Prefiltering in Clock Recovery." IEEE Transactions on Communications 34, no. 11 (1986): 1081–88. http://dx.doi.org/10.1109/tcom.1986.1096462.

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12

Guenter, Brian, and Jack Tumblin. "Quadrature prefiltering for high quality antialiasing." ACM Transactions on Graphics 15, no. 4 (1996): 332–53. http://dx.doi.org/10.1145/234535.234540.

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13

Xiang-Gen Xia, C. C. J. Kuo, and Zhen Zhang. "Wavelet coefficient computation with optimal prefiltering." IEEE Transactions on Signal Processing 42, no. 8 (1994): 2191–97. http://dx.doi.org/10.1109/78.301858.

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14

Laakso, T. I., and S. J. Ovaska. "Prefiltering approach for optimal polynomial prediction." IEEE Transactions on Signal Processing 44, no. 3 (1996): 701–5. http://dx.doi.org/10.1109/78.489043.

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15

Lin, Zhouchen, Hai-Tao Chen, Heung-Yeung Shum, and Jian Wang. "Prefiltering Two-Dimensional Polygons without Clipping." Journal of Graphics Tools 10, no. 1 (2005): 17–26. http://dx.doi.org/10.1080/2151237x.2005.10129189.

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16

Mitchell, R. L. "Prefiltering: cascaded stages of decimation-by-two." IEEE Transactions on Aerospace and Electronic Systems 25, no. 3 (1989): 422–24. http://dx.doi.org/10.1109/7.30798.

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17

Sills, Timothy G. "Prefiltering synthetic imagery by three-dimensional blurring." Optical Engineering 45, no. 3 (2006): 036404. http://dx.doi.org/10.1117/1.2185104.

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18

Ovaska, S. J. "Multistage digital prefiltering of noisy tachometer signals." IEEE Transactions on Instrumentation and Measurement 37, no. 3 (1988): 466–68. http://dx.doi.org/10.1109/19.7478.

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19

Xia Hong, Sheng Chen, and Chris J. Harris. "Elastic-Net Prefiltering for Two-Class Classification." IEEE Transactions on Cybernetics 43, no. 1 (2013): 286–95. http://dx.doi.org/10.1109/tsmcb.2012.2205677.

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20

Da Costa Oliveira, Gustavo Henrique, Wagner Caradori Do Amaral, and Luiz Gimeno Latre. "A robustness approach for prefiltering the GPC." Computers & Electrical Engineering 22, no. 5 (1996): 315–24. http://dx.doi.org/10.1016/s0045-7906(96)00012-2.

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21

Hildebrand, R., A. Lecchini, G. Solari, and M. Gevers. "Optimal prefiltering in iterative feedback tuning 1." IFAC Proceedings Volumes 36, no. 16 (2003): 489–94. http://dx.doi.org/10.1016/s1474-6670(17)34809-7.

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22

Dai, Li, Yousai Zhang, and Yuanjiang Li. "Image Denoising Using BM3D Combining Tetrolet Prefiltering." Information Technology Journal 12, no. 10 (2013): 1995–2001. http://dx.doi.org/10.3923/itj.2013.1995.2001.

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23

Ventalon, Cathie, Rainer Heintzmann, and Jerome Mertz. "Dynamic speckle illumination microscopy with wavelet prefiltering." Optics Letters 32, no. 11 (2007): 1417. http://dx.doi.org/10.1364/ol.32.001417.

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24

Caso, Giuseppe, Luca De Nardis, Mai T. Phuong Le, Flavio Maschietti, Jocelyn Fiorina, and Maria-Gabriella Di Benedetto. "Performance Evaluation of Non-prefiltering vs. Time Reversal Prefiltering in Distributed and Uncoordinated IR-UWB Ad-Hoc Networks." Mobile Networks and Applications 22, no. 5 (2017): 796–805. http://dx.doi.org/10.1007/s11036-017-0829-6.

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25

Boon, John D. "Reducing Wave-Induced Microwave Water-Level Measurement Error with a Least Squares–Designed Digital Filter*." Journal of Atmospheric and Oceanic Technology 31, no. 2 (2014): 491–502. http://dx.doi.org/10.1175/jtech-d-13-00160.1.

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Abstract A microwave water-level sensor, the Design Analysis model H-3611i, will soon enter service at tide stations operated by the National Oceanic and Atmospheric Administration’s Center for Operational Oceanographic Products and Services (CO-OPS) as part of the National Water Level Observation Network. CO-OPS tests include a multisensor deployment at the U.S. Army Corps of Engineers Field Research Facility at Duck, North Carolina, to evaluate microwave water-level measurement error over a wide range of Atlantic Ocean sea states. In situ precision and accuracy of processed (6-min average) water level is found to depend on sea state in addition to data processing methods and sensor operating mode. Estimates over selected 6-h measurement periods show that a degree-two polynomial successfully models the increase in sensor standard error with increasing zero-moment (Hm0) wave height but with differences in rate of error increase dependent on the application of a prefilter and choice of sensor operating mode. Prefiltering of 1-Hz “fast mode” sensor output to remove variance at selected wind-wave frequencies can reduce standard error during extreme conditions (Hm0 ≈ 3 m) from approximately ±3 cm without prefiltering to about ±1 cm using a least squares–designed (LSD) digital filter with a 60-s cutoff period. When wave heights are elevated, skewed non-Gaussian distributions develop within the 1-Hz (360 s) sample domain wherein a 3σ outlier elimination process applied without prefiltering can introduce a negative bias of up to 5 cm in individual 6-min water-level averages.

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26

Soto, Ricardo, Broderick Crawford, Cristian Galleguillos, Eric Monfroy, and Fernando Paredes. "A Prefiltered Cuckoo Search Algorithm with Geometric Operators for Solving Sudoku Problems." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/465359.

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The Sudoku is a famous logic-placement game, originally popularized in Japan and today widely employed as pastime and as testbed for search algorithms. The classic Sudoku consists in filling a9×9grid, divided into nine3×3regions, so that each column, row, and region contains different digits from 1 to 9. This game is known to be NP-complete, with existing various complete and incomplete search algorithms able to solve different instances of it. In this paper, we present a new cuckoo search algorithm for solving Sudoku puzzles combining prefiltering phases and geometric operations. The geometric operators allow one to correctly move toward promising regions of the combinatorial space, while the prefiltering phases are able to previously delete from domains the values that do not conduct to any feasible solution. This integration leads to a more efficient domain filtering and as a consequence to a faster solving process. We illustrate encouraging experimental results where our approach noticeably competes with the best approximate methods reported in the literature.

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27

Li, Tian Ze, Ke Ping Hu, Luan Hou, Chuan Jiang, Heng Wei Lu, and Xia Zhang. "A Technology of Composite Real-Time Image Processing of Profile Measurement." Advanced Materials Research 383-390 (November 2011): 4884–88. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.4884.

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This paper, innovative approaches are based on laser scanning and image analysis, presents an efficient composite technique for object profile extraction with real-time image processing. High throughput is obtained by algorithmic prefiltering to restrict the image area, while high accuracy is achieved by neural recontruction of the profile.

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28

Retamosa, Germán, Luis de Pedro, Ivan González, and Javier Tamames. "Prefiltering Model for Homology Detection Algorithms on GPU." Evolutionary Bioinformatics 12 (January 2016): EBO.S40877. http://dx.doi.org/10.4137/ebo.s40877.

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29

Lin, Chengyu, Meizheng Zhu, and Jiarun Wang. "Efficient Texture Mask Prefiltering in Global Volume Rendering." Journal of Physics: Conference Series 2010, no. 1 (2021): 012053. http://dx.doi.org/10.1088/1742-6596/2010/1/012053.

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30

Borah, D. K., R. A. Kennedy, Zhi Ding, and I. Fijalkow. "Sampling and prefiltering effects on blind equalizer design." IEEE Transactions on Signal Processing 49, no. 1 (2001): 209–18. http://dx.doi.org/10.1109/78.890364.

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31

Rakowski, Waldemar. "Prefiltering in Wavelet Analysis Applying Cubic B-Splines." International Journal of Electronics and Telecommunications 60, no. 4 (2014): 331–40. http://dx.doi.org/10.2478/eletel-2014-0044.

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Abstract Wavelet transform algorithms (Mallat’s algorithm, `a trous algorithm) require input data in the form of a sequence of numbers equal to the signal projection coefficients on a space spanned by integer-translated copies of a scaling function. After sampling of the continuous-time signal, it is most frequently possible to compute only approximated values of the signal projection coefficients by choosing a specific signal approximation. Calculation of the signal projection coefficients based on the signal interpolation by means of cubic B-splines is proposed in the paper.

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32

Lovera, M., L. Piroddi, and W. Spinelli. "ON SAMPLING AND PREFILTERING IN NONLINEAR SYSTEM IDENTIFICATION." IFAC Proceedings Volumes 39, no. 1 (2006): 1009–14. http://dx.doi.org/10.3182/20060329-3-au-2901.00161.

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33

Mu-Hsin Wei, Waymond R. Scott, and James H. McClellan. "Adaptive Prefiltering for Nonnegative Discrete Spectrum of Relaxations." IEEE Geoscience and Remote Sensing Letters 12, no. 5 (2015): 1018–22. http://dx.doi.org/10.1109/lgrs.2014.2374165.

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34

Zhang, C. Q., and X. M. Zhang. "A Prefiltering Instrumental Algorithm Using Sinusoidal Test Signals." IFAC Proceedings Volumes 18, no. 5 (1985): 1971–76. http://dx.doi.org/10.1016/s1474-6670(17)60858-9.

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35

Yellott, J. "Mitigating visual defocus by prefiltering the object spectrum." Journal of Vision 8, no. 17 (2010): 91. http://dx.doi.org/10.1167/8.17.91.

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36

Mizuuchi, Takayuki, Kaoru Okabe, Hareo Hamada, and Tanetoshi Miura. "Prefiltering method for a head‐related stereophonic system." Journal of the Acoustical Society of America 84, S1 (1988): S79. http://dx.doi.org/10.1121/1.2026487.

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37

Tanaka, Y., M. Hasegawa, S. Kato, M. Ikehara, and T. Q. Nguyen. "Adaptive Directional Wavelet Transform Based on Directional Prefiltering." IEEE Transactions on Image Processing 19, no. 4 (2010): 934–45. http://dx.doi.org/10.1109/tip.2009.2038820.

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38

Hanaoka, Chihiro, and Akira Taguchi. "An error diffusion algorithm with data-dependent prefiltering." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 89, no. 5 (2006): 1–11. http://dx.doi.org/10.1002/ecjc.20214.

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39

Weier, Philippe, Tobias Zirr, Anton Kaplanyan, Ling-Qi Yan, and Philipp Slusallek. "Neural Prefiltering for Correlation-Aware Levels of Detail." ACM Transactions on Graphics 42, no. 4 (2023): 1–16. http://dx.doi.org/10.1145/3592443.

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We introduce a practical general-purpose neural appearance filtering pipeline for physically-based rendering. We tackle the previously difficult challenge of aggregating visibility across many levels of detail from local information only, without relying on learning visibility for the entire scene. The high adaptivity of neural representations allows us to retain geometric correlations along rays and thus avoid light leaks. Common approaches to prefiltering decompose the appearance of a scene into volumetric representations with physically-motivated parameters, where the inflexibility of the fitted models limits rendering accuracy. We avoid assumptions on particular types of geometry or materials, bypassing any special-case decompositions. Instead, we directly learn a compressed representation of the intra-voxel light transport. For such high-dimensional functions, neural networks have proven to be useful representations. To satisfy the opposing constraints of prefiltered appearance and correlation-preserving point-to-point visibility, we use two small independent networks on a sparse multi-level voxel grid. Each network requires 10--20 minutes of training to learn the appearance of an asset across levels of detail. Our method achieves 70--95% compression ratios and around 25% of quality improvements over previous work. We reach interactive to real-time framerates, depending on the level of detail.

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40

Philippe, Weier, Zirr Tobias, Kaplanyan Anton, Yan Ling-Qi, and Slusallek Philipp. "Neural Prefiltering for Correlation-Aware Levels of Detail." ACM Transactions on Graphics 42, no. 4 (2023): 1–16. https://doi.org/10.1145/3592443.

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We introduce a practical general-purpose neural appearance filtering pipeline for physically-based rendering. We tackle the previously difficult challenge of aggregating visibility across many levels of detail from local information only, without relying on learning visibility for the entire scene. The high adaptivity of neural representations allows us to retain geometric correlations along rays and thus avoid light leaks. Common approaches to prefiltering decompose the appearance of a scene into volumetric representations with physically-motivated parameters, where the inflexibility of the fitted models limits rendering accuracy. We avoid assumptions on particular types of geometry or materials, bypassing any special-case decompositions. Instead, we directly learn a compressed representation of the intra-voxel light transport. For such high-dimensional functions, neural networks have proven to be useful representations. To satisfy the opposing constraints of prefiltered appearance and correlation-preserving point-to-point visibility, we use two small independent networks on a sparse multi-level voxel grid. Each network requires 10-20 minutes of training to learn the appearance of an asset across levels of detail. Our method achieves 70-95% compression ratios and around 25% of quality improvements over previous work. We reach interactive to real-time framerates, depending on the level of detail.

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41

Khalaf, Osamah Ibrahim, Carlos Andrés Tavera Romero, A. Azhagu Jaisudhan Pazhani, and G. Vinuja. "VLSI Implementation of a High-Performance Nonlinear Image Scaling Algorithm." Journal of Healthcare Engineering 2021 (July 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/6297856.

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This study implements the VLSI architecture for nonlinear-based picture scaling that is minimal in complexity and memory efficient. Image scaling is used to increase or decrease the size of an image in order to map the resolution of different devices, particularly cameras and printers. Larger memory and greater power are also necessary to produce high-resolution photographs. As a result, the goal of this project is to create a memory-efficient low-power image scaling methodology based on the effective weighted median interpolation methodology. Prefiltering is employed in linear interpolation scaling methods to improve the visual quality of the scaled image in noisy environments. By decreasing the blurring effect, the prefilter performs smoothing and sharpening processes to produce high-quality scaled images. Despite the fact that prefiltering requires more processing resources, the suggested solution scales via effective weighted median interpolation, which reduces noise intrinsically. As a result, a low-cost VLSI architecture can be created. The results of simulations reveal that the effective weighted median interpolation outperforms other existing approaches.

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42

Deng, Hong, Yang Liu, Beibei Wang, et al. "Constant-Cost Spatio-Angular Prefiltering of Glinty Appearance Using Tensor Decomposition." ACM Transactions on Graphics 41, no. 2 (2022): 1–17. http://dx.doi.org/10.1145/3507915.

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The detailed glinty appearance from complex surface microstructures enhances the level of realism but is both - and time-consuming to render, especially when viewed from far away (large spatial coverage) and/or illuminated by area lights (large angular coverage). In this article, we formulate the glinty appearance rendering process as a spatio-angular range query problem of the Normal Distribution Functions (NDFs), and introduce an efficient spatio-angular prefiltering solution to it. We start by exhaustively precomputing all possible NDFs with differently sized positional coverages. Then we compress the precomputed data using tensor rank decomposition, which enables accurate and fast angular range queries. With our spatio-angular prefiltering scheme, we are able to solve both the storage and performance issues at the same time, leading to efficient rendering of glinty appearance with both constant storage and constant performance, regardless of the range of spatio-angular queries. Finally, we demonstrate that our method easily applies to practical rendering applications that were traditionally considered difficult. For example, efficient bidirectional reflection distribution function evaluation accurate NDF importance sampling, fast global illumination between glinty objects, high-frequency preserving rendering with environment lighting, and tile-based synthesis of glinty appearance.

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43

Singh, Tarunraj, and Tomáš Vyhlídal. "Recent Results in Reference Prefiltering for Precision Motion Control." IFAC-PapersOnLine 53, no. 2 (2020): 8656–67. http://dx.doi.org/10.1016/j.ifacol.2020.12.315.

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44

SHIMAMURA, Tetsuya, and Jouji SUZUKI. "Input-Dependent FIR Prefiltering for Two-Dimensional Spectral Estimation." Transactions of the Society of Instrument and Control Engineers 28, no. 9 (1992): 1038–45. http://dx.doi.org/10.9746/sicetr1965.28.1038.

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45

Pawlak, M., E. Rafajlowicz, and A. Krzyzak. "Postfiltering versus prefiltering for signal recovery from noisy samples." IEEE Transactions on Information Theory 49, no. 12 (2003): 3195–212. http://dx.doi.org/10.1109/tit.2003.820013.

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46

Spinelli, W., L. Piroddi, and M. Lovera. "On the role of prefiltering in nonlinear system identification." IEEE Transactions on Automatic Control 50, no. 10 (2005): 1597–602. http://dx.doi.org/10.1109/tac.2005.856655.

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47

Jang, Jinik. "Enhanced motion estimation algorithm with prefiltering in video compression." Optical Engineering 51, no. 3 (2012): 037002. http://dx.doi.org/10.1117/1.oe.51.3.037002.

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48

Kaelin, A. N., A. G. Lindgren, and G. S. Moschytz. "Simplified adaptive IIR filters based on optimized orthogonal prefiltering." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 42, no. 5 (1995): 326–33. http://dx.doi.org/10.1109/82.386172.

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49

Liang, Y. C., F. Chin, and W. S. Leon. "Statistical Prefiltering for OFDM Systems Using Multiple Transmit Antennas." IEEE Transactions on Vehicular Technology 55, no. 4 (2006): 1215–23. http://dx.doi.org/10.1109/tvt.2006.877458.

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50

Botella, F., J. Rosa-Herranz, J. J. Giner, S. Molina, and J. J. Galiana-Merino. "A real-time earthquake detector with prefiltering by wavelets." Computers & Geosciences 29, no. 7 (2003): 911–19. http://dx.doi.org/10.1016/s0098-3004(03)00099-2.

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