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Journal articles on the topic 'TRILATERAL FILTER'

Author: Grafiati

Published: 11 September 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 (December 5, 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, this paper computationally determines the optimized values of the spatial, radiometric, ROAD and joint impulsivity variance of the Trilateral filter (TF) for maximum performance. In the experiment, nine noisy standard images (Girl-Tiffany, Pepper, Baboon, House, Resolution, Lena, Airplain, Mobile and Pentagon) under both five power-level Gaussian noise setting and five density impulsive noise setting, are used for estimating optimized parameters of Trilateral filter and for demonstrating the its overall performance, which is compared with classical noise removal techniques such as median filter, linear smoothing filter and Bilateral filter (BF). From the noise removal results of empirically experiments with the highest PSNR criterion, the trilateral filter with the optimized parameters has the superior performance because the ROAD variance and joint impulsivity variance can be statistically analyzed and estimated for each experimental case.

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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 (August 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 filter and local statistics of the image has been developed. The local speckle content of the image influences the trilateral filtering. The trilateral filter is a robust edge preserving filter which considers the similarity of neighboring regions in terms of adjacency, intensity and edge details. Hence, the new method preserves the finer details of the ultrasound images in the process of filtering speckle noise. The proposed technique is validated using synthetic, simulated and real-time clinical ultrasound images. Comparison of the proposed technique with the existing speckle removal algorithms in terms of quality metrics such as MSE, PSNR, UQI, SSI, FoM has been made and best results are obtained for the proposed technique.

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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 (July 25, 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 (July 30, 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 restricted throughout the segmentation of the disease using hybrid fuzzy morphological procedures. To extract the deep seismic features, the Laplacian pyramid decomposition method was utilized for the segmented image. This paper covers an overall analysis of non-local trilateral filter Processing, hybrid fuzzy morphological techniques and analysis of patient and disease characteristics of LIDR- IDRI and FDA data of Group A (no co-AGA), P-value, Multi-mut Patient, Group B (with a co-AGA).

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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 (August 31, 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 (January 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 trilateral filter for mixed noise removal. Simulation results show that our noise detector has a high classification rate, especially for salt-and-pepper noise. And the proposed approach achieves great results both in terms of quantitative measures of signal restoration and qualitative judgments of image quality. In addition, the computational complexity of the proposed method is less than that of many other mixed noise filters.

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8

Wang, Tao. "JPEG2000 image postprocessing with novel trilateral deringing filter." Optical Engineering 47, no. 2 (February 1, 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 (January 8, 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 (December 30, 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 (January 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 (June 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 (December 28, 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 and Gaussian noise, as much as mixed noise. The proposed filtering method was compared to the state-of-the-art methodologies: adaptive nearest neighbor filter, alternating projections filter, color block-matching 3D filter, fuzzy peer group averaging filter, partition-based trimmed vector median filter, trilateral filter, fuzzy wavelet shrinkage denoising filter, graph regularization filter, iterative peer group switching vector filter, peer group method, and the fuzzy vector median method. The experiments demonstrated that the introduced noise reduction technique outperforms those state-of-the-art filters with respect to the metrics peak signal to noise ratio (PSNR), the mean absolute error (MAE), and the normalized color difference (NCD).

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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 (January 5, 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 (June 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 (June 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 (January 13, 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 (February 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 mixed noise efficiently. In this paper, we propose a smart switching bilateral filter (SSBF) to outperform a demerit of traditional SBF filter. In the first stage of SSBF, we propose a new scheme of noise estimation using domain weight (DW) pattern which characterizes the distribution of the different intensity between a considered pixel and its neighbors. By using this estimation, the types of mixed noises and their strength are estimated accurately. The filter parameters of SBF are selected from the table where the spatial weight and radiometric weight are already learned. As a result, SSBF can improve the performance of traditional SBF and can remove mixed noises efficiently without knowing the exact type of contaminated mixed noise. Moreover, the performance of SSBF is compared to the optimal SBF filter (OSBF) where OSBF sets the optimal value of filter parameters on the contaminated mixed noise and three new filters — block-matching and 3D filtering (BM3D), nonlocal sparse representation (NCSR), and trilateral filter (TF). The simulation results showed that the performance of SSBF outperforms BM3D, NCSR, TF, and SBF and is near to optimal SBF filter, even if the SSBF does not know the type of mixed noise.

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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 (January 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 the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.

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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 (May 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 (June 22, 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, and others. The paper gives a clear description of edge-preserving filters, from the heat diffusion equation in ancient times to the present, explaining their numerous applications and detailing their numerous uses. Additionally, mathematical analysis is included, as well as efficient and optimized implementations. The focus is on preserving the boundaries, spikes, and canyons, and the information is given clearly and in detail. Finally, it offers a realistic representation of efficient implementation, as well as a comprehensive research scope for future hardware implementation.

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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 (February 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 (May 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 (February 28, 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 fields and among these techniques cannot perform perfectly every turn. The paper focuses on 3D vision, introduce the background and process of 3D vision, reviews several classical datasets in the field of 3D vision, based on which the learning approaches and several types of applications of 3D vision were evaluated and analyzed.

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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 (October 17, 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 (December 12, 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 (April 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 indicator range value scheme, a centroid algorithm based on trilateral measurement positioning, and a weighting factor to improve the positioning accuracy of the trilateral centroid positioning algorithm. Compared with the centroid algorithm, the Min-Max multilateration algorithm, and the weighted centroid based on distance algorithm, the simulation results showed that (1) the positioning performance of the quadratic weighted centroid algorithm was superior to the other three algorithms; (2) when the reference nodes were symmetrically arranged, the positioning accuracy was higher than a fold line layout; and (3) when the lateral reference node spacing was extended from 20 to 30 m, the average positioning error met positioning accuracy requirements, which could reduce overall system costs.

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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 (May 7, 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 space is denoising using improved trilateral filter in adaptive angle threshold. A series of numerical experiment proved the new denoising algorithm in this paper achieved more detail feature and smoother surface.

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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 (June 8, 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 Euclidean distance between the targets and WiFi scanners by replacing the common propagation model (PM). The online stage includes three procedures. Firstly, a constant velocity Kalman filter (CVKF) is developed to smooth the real-time RSSI time series and estimate the target-detector distance. Then, a least squares Taylor series expansion (LS-TSE) is developed to calculate the actual 2-dimensional coordinate with the replacement of existing trilateral localization. Thirdly, a trajectory-based technique of the unscented Kalman filter (UKF) is introduced to smooth estimated positioning points. In tests that used field scenarios from Guangzhou, China, the experiments demonstrate that the combined CVKF and PPRM can achieve the highly accurate distance estimator of <1.98 m error with the probability of 90% or larger, which outperforms the existing propagation model. In addition, the online method can achieve average positioning error of 1.67 m with the much better than classical methods.

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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 frames, are distorted seriously. In this paper, we are considering use of the modern image processing methods to determine the mineral location and to recognize the mineral actually within a little computation complex. We firstly analyze the recent underwater imaging models and propose a novel underwater optical imaging model, which is much closer to the light propagation model in the underwater environment. In our imaging system, we remove the electrical noise by dual-tree complex wavelet transform. And then we solve the nonuniform illumination of artificial lights by fast guided trilateral bilateral filter and recover the image color through automatic color equalization. Finally, a shape-based mineral recognition algorithm is proposed for underwater objects detection. These methods are designed for real-time execution on limited-memory platforms. This pipeline is suitable for detecting underwater objects in practice by our experiences. The initial results are presented and experiments demonstrate the effectiveness of the proposed real-time visualization system.

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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 (November 4, 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 solve this problem, this paper proposes a filtering algorithm for mixed noise with salt and pepper + Gaussian noise that combines an improved median filtering algorithm, an improved wavelet threshold denoising algorithm and an improved Non-local Means (NLM) algorithm. The algorithm makes full use of the advantages of the median filter in removing salt and pepper noise and demonstrates the good performance of the wavelet threshold denoising algorithm and NLM algorithm in filtering Gaussian noise. At first, we made improvements to the three algorithms individually, and then combined them according to a certain process to obtain a new method for removing mixed noise. Specifically, we adjusted the size of window of the median filtering algorithm and improved the method of detecting noise points. We improved the threshold function of the wavelet threshold algorithm, analyzed its relevant mathematical characteristics, and finally gave an adaptive threshold. For the NLM algorithm, we improved its Euclidean distance function and the corresponding distance weight function. In order to test the denoising effect of this method, salt and pepper + Gaussian noise with different noise levels were added to the test images, and several state-of-the-art denoising algorithms were selected to compare with our algorithm, including K-Singular Value Decomposition (KSVD), Non-locally Centralized Sparse Representation (NCSR), Structured Overcomplete Sparsifying Transform Model with Block Cosparsity (OCTOBOS), Trilateral Weighted Sparse Coding (TWSC), Block Matching and 3D Filtering (BM3D), and Weighted Nuclear Norm Minimization (WNNM). Experimental results show that our proposed algorithm is about 2–7 dB higher than the above algorithms in Peak Signal-Noise Ratio (PSNR), and also has better performance in Root Mean Square Error (RMSE), Structural Similarity (SSIM), and Feature Similarity (FSIM). In general, our algorithm has better denoising performance, better restoration of image details and edge information, and stronger robustness than the above-mentioned algorithms.

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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 (July 30, 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 diperoleh menjadi kurang akurat. Sehubungan dengan hal tersebut maka setelah dilakukan perhitungan dengan trilaterasi, dilanjutkan dengan menambahkan metode Kalman Filter untuk meningkatkan nilai akurasi. Penelitian ini menggunakan BLE (Bluetooth Low Energy) sebagai transmitter, sedangkan receiver menggunakan smartphone yang sudah ter-install aplikasi untuk menerima nilai RSSI. Setelah menggunakan Kalman Filter diperoleh peningkatan nilai akurasi sebesar 0, 1 meter dari nilai perhitungan trilaterasi

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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 (June 1, 2018): 2089–91. http://dx.doi.org/10.1166/jctn.2018.7412.

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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 trilateral filter on real time execution. In this manuscript, we intend to design and implement the FPGA architecture using an advanced trilateral filter. Biomedical images with different noises are used during implementation and compared with the existing bilateral and trilateral architecture to assess the proposed architecture performance. For evaluating the performance metrics of the proposed advanced trilateral filter on MATLAB platform, peak signal-to-noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) are calculated for different biomedical images – such as brain (MRI), chest (x-ray) and lungs (CT) – with different noises – such as salt and pepper, Gaussian, Poisson and Speckle noises – compared with existing bilateral filter and trilateral filter, respectively. The proposed advanced trilateral filter implementations are checked on Virtex-6, Virtex-7 and Zynq FPGA development board using Verilog programming language in Xilinx ISE 14.5 design tools. The simulation outcomes display that the FPGA execution of the advanced trilateral filter contains better noise removal efficiency in biomedical images compared with the existing bilateral and trilateral filter.

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Ai, Jiaqiu, Gang Wang, Gaowei Fan, Feifan Wang, Lu Jia, Yanlan Wu, and Enbing Hou. "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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"Retinex Color Balanced Piecewise Contrast and Fuzzy Trilateral Filter for Underwater Image Enhancement." International Journal of Recent Technology and Engineering 8, no. 4 (November 30, 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, to start with, a color correction model based on the Retinex multi proportions is presented. With the color corrected output obtained, an Eigen-based White Balancing method is applied to generate color balanced model. With the color balanced underwater image, color contrasting is performed using the Piecewise Linear Color Contrast model. After obtaining the latter, the contrast is said to be improved to a better level. Finally, to generate a haze-free image a Fuzzy Reinforced Trilateral filter is applied. The enhanced and de-hazed images are distinguished by reduced noise level, thus enhanced visibility and contrast while the finest edges are enhanced. The proposed RCP-FRTF method provides better performance in terms of PSNR, computational time, complexity and accuracy as compared to conventional methods.

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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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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 (July 24, 2017). http://dx.doi.org/10.17577/ijertv6is070233.

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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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