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Artykuły w czasopismach na temat „TRILATERAL FILTER”

Autor: Grafiati

Data publikacji: 11 września 2023

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1

Patanavijit, Vorapoj, and Kornkamol Thakulsukanant. "An Empirical Evaluation of the Parameters of Trilateral Filter for Noise Removal Implementation on Gaussian and Impulsive Noise." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 11, no. 2 (2017): 190–202. http://dx.doi.org/10.37936/ecti-cit.2017112.61688.

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Since 1998, the Bilateral filter (BF) is worldwide accepted for its performance in practical point of view under Gaussian noise however the Bilateral filter has a poor performance for impulsive noise. Based on the combining of the Rank-Ordered Absolute Differences (ROAD) detection technique and the Bilateral filter for automatically reducing or persecuting of impulsive and Gaussian noise, this Trilateral filter (TF) has been proposed by Roman Garnett et al. since 2005 but the Trilateral filter efficiency is rest absolutely on spatial, radiometric, ROAD and joint impulsivity variance. Hence, th

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2

Jayanthi Sree, S., and C. Vasanthanayaki. "De-Speckling of Ultrasound Images Using Local Statistics-Based Trilateral Filter." Journal of Circuits, Systems and Computers 28, no. 09 (2019): 1950150. http://dx.doi.org/10.1142/s0218126619501500.

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Speckle noise in ultrasound images is a major hindrance for the automation of segmentation, detection, classification and measurements of region of interest, to assist clinician for diagnosing pathologies. Speckle noise occurs due to constructive and destructive interference of the echo signals reflected from the target and has a granular appearance. Various techniques have been devised for speckle reduction. Most of these techniques are based on adaptive filters, wavelet transform and anisotropic diffusion filters. In this paper, a new speckle reduction technique based on the trilateral filte

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3

Zhang, Xiaohua, Yuelan Xin, and Ning Xie. "Anisotropic Joint Trilateral Rolling Filter for Image Smoothing." Journal of the Institute of Industrial Applications Engineers 7, no. 3 (2019): 91–98. http://dx.doi.org/10.12792/jiiae.7.91.

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4

Reddy, N. Sudhir, and V. Khanaa. "Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network." International Journal of Experimental Research and Review 31, Spl Volume (2023): 12–22. http://dx.doi.org/10.52756/10.52756/ijerr.2023.v31spl.002.

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Lung cancer is one of the major illnesses that contribute to millions of fatalities worldwide. Numerous deaths could be saved through the early identification and categorization of lung cancers. However, with traditional approaches, classification accuracy cannot be produced. To detect and classify lung diseases, a deep learning convolutional neural network model has been developed. LDDC, the customized local trilateral filter, is used for pre-processing the lung images from computing tomography for non-local trilateral filters. The region of interest for lung cancer was successfully restricte

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5

Kesireddy, Akitha, and Mohamed El-Sharkawy. "Adaptive Trilateral Filter for HEVC Standard." International journal of Multimedia & Its Applications 6, no. 4 (2014): 19–26. http://dx.doi.org/10.5121/ijma.2014.6402.

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6

Serikawa, Seiichi, and Huimin Lu. "Underwater image dehazing using joint trilateral filter." Computers & Electrical Engineering 40, no. 1 (2014): 41–50. http://dx.doi.org/10.1016/j.compeleceng.2013.10.016.

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7

Chen, Shuhan, Weiren Shi, and Wenjie Zhang. "An Efficient Universal Noise Removal Algorithm Combining Spatial Gradient and Impulse Statistic." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/480274.

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We propose a novel universal noise removal algorithm by combining spatial gradient and a new impulse statistic into the trilateral filter. By introducing a reference image, an impulse statistic is proposed, which is called directional absolute relative differences (DARD) statistic. Operation was carried out in two stages: getting reference image and image denoising. For denoising, we introduce the spatial gradient into the Gaussian filtering framework for Gaussian noise removal and integrate our DARD statistic for impulse noise removal, and finally we combine them together to create a new tril

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8

Wang, Tao. "JPEG2000 image postprocessing with novel trilateral deringing filter." Optical Engineering 47, no. 2 (2008): 027005. http://dx.doi.org/10.1117/1.2844731.

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9

Hu, Chunyue, Dongyang Li, Zhenyan Sun, Ning Zhang, and Jianjun Lei. "Region-based trilateral filter for depth video coding." International Journal of Embedded Systems 11, no. 2 (2019): 163. http://dx.doi.org/10.1504/ijes.2019.098293.

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10

Lei, Jianjun, Ning Zhang, Zhenyan Sun, Dongyang Li, and Chunyue Hu. "Region-based trilateral filter for depth video coding." International Journal of Embedded Systems 11, no. 2 (2019): 163. http://dx.doi.org/10.1504/ijes.2019.10019711.

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11

Bajaj, Kanika, and Navjot Kaur. "Integrated Gabor Filter and Trilateral Filter for Exudate Extraction in Fundus Images." International Journal of Image, Graphics and Signal Processing 9, no. 1 (2017): 10–17. http://dx.doi.org/10.5815/ijigsp.2017.01.02.

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12

Oishi, Shuji, Ryo Kurazume, Yumi Iwashita, and Tsutomu Hasegawa. "Smoothing Range Image using Trilateral Filter and Reflectance Image." IEEJ Transactions on Electronics, Information and Systems 132, no. 2 (2012): 291–98. http://dx.doi.org/10.1541/ieejeiss.132.291.

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13

Kim, Taekwon, and Kiyun Kim. "A new demosaicing method based on trilateral filter approach." Journal of the Korea Society of Digital Industry and Information Management 11, no. 4 (2015): 155–64. http://dx.doi.org/10.17662/ksdim.2015.11.4.155.

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14

Lo, Kai-Han, Yu-Chiang Frank Wang, and Kai-Lung Hua. "Edge-Preserving Depth Map Upsampling by Joint Trilateral Filter." IEEE Transactions on Cybernetics 48, no. 1 (2018): 371–84. http://dx.doi.org/10.1109/tcyb.2016.2637661.

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15

Lu, Qingchun, and Xiangzhong Fang. "Joint trilateral motion vector filter for bidirectional motion compensation." Electronics Letters 49, no. 13 (2013): 798–800. http://dx.doi.org/10.1049/el.2013.0995.

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16

Arnal, Josep, and Luis Súcar. "Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images." Applied Sciences 10, no. 1 (2019): 243. http://dx.doi.org/10.3390/app10010243.

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To decrease contamination from a mixed combination of impulse and Gaussian noise on color digital images, a novel hybrid filter is proposed. The new technique is composed of two stages. A filter based on a fuzzy metric is used for the reduction of impulse noise at the first stage. At the second stage, to remove Gaussian noise, a fuzzy peer group method is applied on the image generated from the previous stage. The performance of the introduced algorithm was evaluated on standard test images employing widely used objective quality metrics. The new approach can efficiently reduce both impulse an

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17

Cui, Wenchao, Mengmeng Li, Guoqiang Gong, Ke Lu, Shuifa Sun, and Fangmin Dong. "Guided trilateral filter and its application to ultrasound image despeckling." Biomedical Signal Processing and Control 55 (January 2020): 101625. http://dx.doi.org/10.1016/j.bspc.2019.101625.

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18

Singh, Dilbag, and Vijay Kumar. "Defogging of road images using gain coefficient-based trilateral filter." Journal of Electronic Imaging 27, no. 01 (2018): 1. http://dx.doi.org/10.1117/1.jei.27.1.013004.

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19

Onuki, Masaki, Shunsuke Ono, Masao Yamagishi, and Yuichi Tanaka. "Graph Signal Denoising via Trilateral Filter on Graph Spectral Domain." IEEE Transactions on Signal and Information Processing over Networks 2, no. 2 (2016): 137–48. http://dx.doi.org/10.1109/tsipn.2016.2532464.

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20

Lim, Ilsoon, Hocheon Wey, Jaejoon Lee, and Dusik Park. "30.4: Trilateral Filter for Depth Map Interpolation in 3D Video." SID Symposium Digest of Technical Papers 43, no. 1 (2012): 403–6. http://dx.doi.org/10.1002/j.2168-0159.2012.tb05801.x.

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21

Chang, Yong-Jun, and Yo-Sung Ho. "Enhanced guided image filter using trilateral kernel for disparity error correction." Electronic Imaging 2019, no. 11 (2019): 257–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.11.ipas-257.

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22

Jung, Seung-Won. "Enhancement of Image and Depth Map Using Adaptive Joint Trilateral Filter." IEEE Transactions on Circuits and Systems for Video Technology 23, no. 2 (2013): 258–69. http://dx.doi.org/10.1109/tcsvt.2012.2203734.

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23

Wang, Ye, You Yang, and Qiong Liu. "Feature-Aware Trilateral Filter With Energy Minimization for 3D Mesh Denoising." IEEE Access 8 (2020): 52232–44. http://dx.doi.org/10.1109/access.2020.2981161.

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24

Langampol, Kriengkri, Kanabadee Srisomboon, Vorapoj Patanavijit, and Wilaiporn Lee. "Smart Switching Bilateral Filter with Estimated Noise Characterization for Mixed Noise Removal." Mathematical Problems in Engineering 2019 (May 28, 2019): 1–23. http://dx.doi.org/10.1155/2019/5632145.

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Traditionally, several existing filters are proposed for removing a specific type of noise. However, in practice, the image communicated through the communication channel may be contaminated with more than one type of noise. Switching bilateral filter (SBF) is proposed for removing mixed noise by detecting a contaminated noise at the concerned pixel and recalculates the filter parameters. Although the filter parameters of SBF are sensitive to type and strength of noise, the traditional SBF filter has not taken the strength into account. Therefore, the traditional SBF filter cannot remove the m

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25

Zhou, Dongsheng, Ruyi Wang, Xin Yang, Qiang Zhang, and Xiaopeng Wei. "Depth image super-resolution reconstruction based on a modified joint trilateral filter." Royal Society Open Science 6, no. 1 (2019): 181074. http://dx.doi.org/10.1098/rsos.181074.

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Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide t

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26

Dongming Chen, Mohsen Ardabilian, and Liming Chen. "A Fast Trilateral Filter-Based Adaptive Support Weight Method for Stereo Matching." IEEE Transactions on Circuits and Systems for Video Technology 25, no. 5 (2015): 730–43. http://dx.doi.org/10.1109/tcsvt.2014.2361422.

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27

Sivakumar, K., Sakthiraam B, Santosh Snehal. V, and Yogashivasankarri S. "Comparative analysis of elementary and edge-preserving spatial filters in noise removal – a comprehensive study." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1244–51. http://dx.doi.org/10.51201/jusst/21/06448.

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Preserving the edges and information is one of the main purposes of edge-preserving filters. That is, they’re employed to smooth a picture, and minimize halos, phantoms, and edge blur over the edge. They have a nonlinear relationship between one thing and another. Bilateral filters, anisotropic diffusion filters, directed filters, and trilateral filters are all types of example filters. The filter family may be used in a wide range of image processing tasks, such as denoising, video abstraction, demosaicing, optical flow estimation, stereo matching, tone mapping, style transfer, relighting, an

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28

Chang, Herng-Hua, Cheng-Yuan Li, and Audrey Haihong Gallogly. "Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration." IEEE Transactions on Biomedical Engineering 65, no. 2 (2018): 400–413. http://dx.doi.org/10.1109/tbme.2017.2772853.

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29

Shen, Jianbing, Shiaofen Fang, Hanli Zhao, Xiaogang Jin, and Hanqiu Sun. "Fast approximation of trilateral filter for tone mapping using a signal processing approach." Signal Processing 89, no. 5 (2009): 901–7. http://dx.doi.org/10.1016/j.sigpro.2008.11.009.

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30

Zhao, Chunhui, Xiaoqing Wan, Genping Zhao, and Yiming Yan. "Spectral–spatial classification of hyperspectral images using trilateral filter and stacked sparse autoencoder." Journal of Applied Remote Sensing 11, no. 1 (2017): 016033. http://dx.doi.org/10.1117/1.jrs.11.016033.

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31

Lu, Luanhao. "A Survey of Learning Approaches and Application for 3D Vision." MATEC Web of Conferences 173 (2018): 03053. http://dx.doi.org/10.1051/matecconf/201817303053.

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Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the two-dimensional (2D) images is the most effective topic in computer vision and video surveillance. Three-dimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fi

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32

Kaur, Kuljeet, and Aarti A. "Performance Evaluation of Data Mining based Images by using Fuzzy, Mean, Median Trilateral Filter." International Journal of Computer Applications 151, no. 9 (2016): 21–25. http://dx.doi.org/10.5120/ijca2016911860.

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33

Singh, Dilbag, and Vijay Kumar. "Dehazing of remote sensing images using fourth‐order partial differential equations based trilateral filter." IET Computer Vision 12, no. 2 (2017): 208–19. http://dx.doi.org/10.1049/iet-cvi.2017.0044.

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34

Wang, Haiying, Linhao Liang, Jian Xu, Hui She, and Wuxiang Li. "A quadratic weighted centroid algorithm for tunnel personnel positioning." International Journal of Distributed Sensor Networks 16, no. 4 (2020): 155014772091702. http://dx.doi.org/10.1177/1550147720917021.

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To improve the accuracy and generalization of tunnel personnel positioning systems, this article proposes a quadratic weighted centroid algorithm. By adopting a Gaussian filter model to improve the range accuracy of the received signal strength indicator algorithm and combining the centroid algorithm and weighting factor with a trilateration positioning model, a quadratic weighted centroid algorithm is proposed to improve the positioning accuracy of unknown positioning nodes. The key ideas behind the quadratic weighted centroid algorithm include an optimization of the received signal strength

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35

Cheng, Yun, Ju Zhang, Jing Yan, and Zhibing Pu. "An Integrated De-Speckling Approach for Medical Ultrasound Images Based on Wavelet and Trilateral Filter." Ultrasound in Medicine & Biology 43 (2017): S5. http://dx.doi.org/10.1016/j.ultrasmedbio.2017.08.955.

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36

Zhang, Ju, Lili Wu, Guangkuo Lin, and Yun Cheng. "An Integrated De-speckling Approach for Medical Ultrasound Images Based on Wavelet and Trilateral Filter." Circuits, Systems, and Signal Processing 36, no. 1 (2016): 297–314. http://dx.doi.org/10.1007/s00034-016-0305-8.

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37

Liang, Xin He, Jin Liang, and Chen Guo. "Scatter Point Cloud Denoising Based on Self-Adaptive Optimal Neighborhood." Advanced Materials Research 97-101 (March 2010): 3631–36. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3631.

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We present a scatter point cloud denoising method, which can reduce noise effectively, while preserving mesh features such as sharp edges and corners. The method consists of two stages. Firstly, noisy points normal are filtered iteratively; second, location noises of points are reduced. How to select proper denoising neighbors is a key problem for scatter point cloud denoising operation. The local shape factor which related to the surface feature is proposed. By using the factor, we achieved the shape adaptive angle threshold and adaptive optimal denoising neighbor. Normal space and location s

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38

Huang, Zilin, Lunhui Xu, and Yongjie Lin. "Multi-Stage Pedestrian Positioning Using Filtered WiFi Scanner Data in an Urban Road Environment." Sensors 20, no. 11 (2020): 3259. http://dx.doi.org/10.3390/s20113259.

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Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the combination of offline RSSI distance estimation and real-time continuous position fitting, which can achieve high-position accuracy in the urban road environment. At the offline stage, the piecewise polynomial regression model (PPRM) is proposed to formulate the Eucli

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39

Srinivasa Reddy, E., and Akula Suneetha. "Improved generalised fuzzy peer group with modified trilateral filter to remove mixed impulse and adaptive white Gaussian noise from colour images." International Journal of Nanotechnology 20, no. 1/2/3/4 (2023): 129. http://dx.doi.org/10.1504/ijnt.2023.10056473.

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40

Suneetha, Akula, and E. Srinivasa Reddy. "Improved generalised fuzzy peer group with modified trilateral filter to remove mixed impulse and adaptive white Gaussian noise from colour images." International Journal of Nanotechnology 20, no. 1/2/3/4 (2023): 129–50. http://dx.doi.org/10.1504/ijnt.2023.131107.

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41

Li, Yujie, Huimin Lu, Lifeng Zhang, Jianru Li, and Seiichi Serikawa. "Real-Time Visualization System for Deep-Sea Surveying." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/437071.

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Remote robotic exploration holds vast potential for gaining knowledge about extreme environments, which is difficult to be accessed by humans. In the last two decades, various underwater devices were developed for detecting the mines and mine-like objects in the deep-sea environment. However, there are some problems in recent equipment, like poor accuracy of mineral objects detection, without real-time processing, and low resolution of underwater video frames. Consequently, the underwater objects recognition is a difficult task, because the physical properties of the medium, the captured video

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42

He, Chun, Ke Guo, and Huayue Chen. "An Improved Image Filtering Algorithm for Mixed Noise." Applied Sciences 11, no. 21 (2021): 10358. http://dx.doi.org/10.3390/app112110358.

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In recent years, image filtering has been a hot research direction in the field of image processing. Experts and scholars have proposed many methods for noise removal in images, and these methods have achieved quite good denoising results. However, most methods are performed on single noise, such as Gaussian noise, salt and pepper noise, multiplicative noise, and so on. For mixed noise removal, such as salt and pepper noise + Gaussian noise, although some methods are currently available, the denoising effect is not ideal, and there are still many places worthy of improvement and promotion. To

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43

Wibowo, Fahrudin, and Aulia Burhanudin. "Penerapan Kalman Filter Pada Metode Trilaterasi Untuk Peningkatan Akurasi Estimasi Perhitungan Jarak Di Dalam Ruangan." Jurnal Ilmiah Betrik 9, no. 02 (2018): 96–102. http://dx.doi.org/10.36050/betrik.v9i02.35.

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Penelitian tentang posisi maupun jarak suatu obyek di dalam ruangan telah banyak dilakukan. Metode trilaterasi adalah salah satu metode yang dapat dipergunakan untuk menghitung nilai estimasi jarak atau posisi suatu obyek di dalam ruangan, berdasarkan nilai RSSI (Received Signal Strength Indication) yang diterima suatu receiver. Namun, nilai RSSI yang diterima tidak dapat stabil dikarenakan sinyal yang diterima oleh receiver sangat dipengaruhi kondisi lingkungan pada ruangan yang pada umumnya memiliki nilai noise yang cukup tinggi. Sehingga dapat berakibat pada nilai estimasi jarak yang dipero

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44

Regan, D., and C. Padmavathi. "A Study on Performance of Bilateral and Trilateral Filters in the Mixed Noise Environment." Journal of Computational and Theoretical Nanoscience 15, no. 6 (2018): 2089–91. http://dx.doi.org/10.1166/jctn.2018.7412.

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45

Ramkumar Raja, M., R. Naveen, Thangam Palaniswamy, TV Mahendiran, Neeraj Kumar Shukla, and Rajesh Verma. "A field programmable gate array-based biomedical noise reduction framework using advanced trilateral filter." Transactions of the Institute of Measurement and Control, August 24, 2021, 014233122110222. http://dx.doi.org/10.1177/01423312211022200.

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Filtering is one of the essential tools utilized to remove undesirable features in biomedical images. Most biomedical image denoising systems are used for clinical diagnosis. So, in this paper, we use the advanced trilateral filter in the field programmable gate array (FPGA) for removing noise in the biomedical image. Generally, the trilateral filter is used as an edge preserving smoothing filter. This advanced approach of trilateral filter gives the best noise diminution and enhances the image quality. This paper also proposes the hardware implementation of an efficient FPGA-based advanced tr

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46

Ai, Jiaqiu, Gang Wang, Gaowei Fan, et al. "A Trilateral Filter for Video SAR Speckle Noise Reduction." IEEE Geoscience and Remote Sensing Letters, 2022, 1. http://dx.doi.org/10.1109/lgrs.2022.3174834.

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47

"Retinex Color Balanced Piecewise Contrast and Fuzzy Trilateral Filter for Underwater Image Enhancement." International Journal of Recent Technology and Engineering 8, no. 4 (2019): 10815–22. http://dx.doi.org/10.35940/ijrte.d4363.118419.

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Over the past few years, underwater observation has become an active research area. Due to the higher rate of image degradation in the underwater environment, image enhancement has become one of the problems to be addressed for the underwater research. Underwater images face limitations like color correction, white balance, color contrast and haze. To overcome those problems, a novel fusion method based on the Retinex Color-balanced Piecewise-contrast and Fuzzy Reinforced Trilateral Filter (RCP-FRTF) method is presented for underwater image improvement. With the underwater image given as input

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48

Wang, Bin, Dongmei Song, Chengcong Hu, and Zhongwei Li. "An adaptive PolSAR trilateral filter based on the mechanism of scattering consistency." IEEE Transactions on Geoscience and Remote Sensing, 2022, 1. http://dx.doi.org/10.1109/tgrs.2022.3201726.

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49

Shilpa Pathania and Mrs. Mandeep Kaur. "Enhanced Feature Extraction for Tumor Detection by using Decision based Joint Trilateral Filter." International Journal of Engineering Research and V6, no. 07 (2017). http://dx.doi.org/10.17577/ijertv6is070233.

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50

Kumari, Naveen, and Rekha Bhatia. "Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter." Soft Computing, February 7, 2022. http://dx.doi.org/10.1007/s00500-022-06804-7.

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