Relevant bibliographies by topics / TRILATERAL FILTER

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Academic literature on the topic 'TRILATERAL FILTER'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "TRILATERAL FILTER"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "TRILATERAL FILTER"

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KAVITA. "IMAGE DENOISING USING MOVING FRAME APPROACH BASED ON TRILATERAL FILTER." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16523.

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Noise corrupts the images and in this manner their quality corrupts. This corruption incorporates concealment of edges, auxiliary points of interest, obscuring limits and so on. There are a few strategies to suppress the noise. The fundamental objective of denoising the image is to protect the critical element, for example, edges, limits and so on. Image compression separating is the way toward expelling noise which annoys image examination techniques. In a few applications like segmentation, denoising is intended to smooth homogeneous areas while safeguarding the shapes. Real-time denoising is required in a great deal of uses like picture guided careful intercessions, video examination and visual serving. Image denoising is finished by separating which can be comprehensively isolated into classes: straight sifting and nonlinear sifting. Mean sifting and Gaussian separating are the case of spatial denoising strategies. They are direct techniques which cause obscuring the images and all the while smother the subtle elements. Denoising is any signal processing strategy which reproduces a signal from a noisy one. Its will probably evacuate noise and safeguard valuable data. Denoising means to diminish noise in homogeneous zones, while safeguarding picture shapes. Denoising is vital for pretreatment techniques, for example, question acknowledgment, division, arrangement and example investigation. Because of the extraordinary surface of ultrasound pictures, their denoising is especially troublesome. Noise lessening is the way toward expelling noise from the flag. Saving the points of interest of a picture and evacuating the irregular noise beyond what many would consider possible is the objective of picture denoising approaches. Numerous fruitful strategies for picture denoising have been produced till date. v Bilateral filtering is a case of nonlinear separating. It is a non-iterative technique. It joins space and range channels all the while. It preserves edge data while denoising. The possibility of reciprocal separating is the calculation of each pixel weight using a spatial piece and its increase utilizing an element of impact in the power space. This last can diminish the pixel weight with substantial power contrasts. By and by, under this shape the channel can't control spot noise. This channel may tend to over smooth edges. Then again, its range channel piece utilized pixel availability, and hence it couldn't be utilized straightforwardly for applications that in truth would overlook spatial connections. These channels go for smoothing the picture to evacuate some type of noise. Anyway it doesn't give agreeable outcomes, genuine dim levels are contaminated truly and the range channel can't work legitimately. The trilateral channel was acquainted as methods with decrease drive noise in pictures. The trilateral channel was reached out to be an angle protecting channel, including the nearby picture inclination into the separating procedure. For the most part, the parameters of Trilateral Filtering are generally dictated by experimentation by and by; along these lines bringing about additional time utilization. In any case, to build the merging rate and to enhance the denoising procedure, we have presented the altered trilateral sifting approach by the ideal determination of its parameters utilizing GWO calculation consequently in view of the denoising execution. At last, we will demonstrate the viability of proposed separating strategy by methods for examining with different noise models.
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Kesireddy, Akitha. "A new adaptive trilateral filter for in-loop filtering." Thesis, 2014. http://hdl.handle.net/1805/5927.

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Indiana University-Purdue University Indianapolis (IUPUI)
HEVC has achieved significant coding efficiency improvement beyond existing video coding standard by employing many new coding tools. Deblocking Filter, Sample Adaptive Offset and Adaptive Loop Filter for in-loop filtering are currently introduced for the HEVC standardization. However these filters are implemented in spatial domain despite the fact of temporal correlation within video sequences. To reduce the artifacts and better align object boundaries in video , a new algorithm in in-loop filtering is proposed. The proposed algorithm is implemented in HM-11.0 software. This proposed algorithm allows an average bitrate reduction of about 0.7% and improves the PSNR of the decoded frame by 0.05%, 0.30% and 0.35% in luminance and chroma.
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Shu-HuiChiu and 邱淑惠. "Super Resolution Using Trilateral Filter Regression with Local Texture Enhancement." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/24fj2y.

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碩士
國立成功大學
電腦與通信工程研究所
104
In this thesis, a super resolution using trilateral filter regression with local texture enhancement is proposed. The system consists of two major parts, the interpolation of trilateral filter regression, and local texture enhancement. The trilateral filter is used to fix the disadvantage of the traditional NEDI methods. In the interpolation part, the suitable weights are given for all pixels in the training window by the trilateral filter. Hence, the blocking effect can be reduced in some regions. After interpolations part, the high frequency information of the local texture is added into the enlarged images to improve the texture blurring. Experimental results demonstrate that the proposed method provides superior performance than other well-known approaches in the average PSNR and SSIM. In addition, the texture enhancement achieves better subjective performance by evaluation of observers.
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Tai, Hung-Shou, and 戴宏碩. "The Hardware Architecture Design of Trilateral Noise Filter for Color Images." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/59667062748549662497.

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碩士
國立臺灣師範大學
應用電子科技研究所
95
Removing noise while preserving and enhancing edges is one of the most fundamental operations of image/video processing. When taking pictures with digital cameras, it is frequently found that the color images are corrupted by miscellaneous noise, especially images get with high ISO values in low luminance. Hence, noise filtering is a necessary module in digital still cameras. The difficulty of designing noise filter is that the filter will also reduce the sharpness of the image. On the other hand, optical lens imperfections are usually equivalent to spatial low pass filters and tend to result in blurred images. It is customary to apply edge enhancement algorithm on the image in order to improve the sharpness, but this process usually increase the noise level as a by-product. Hence, an efficient noise filter is very important before edge enhancement. In this paper, the efficiency of trilateral filter and other popular filters are compared briefly, and the trilateral filter is implemented by HDL language for image processing chip.
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Hsieh, Tung-Ju, and 謝東儒. "Rician Noise Removal in MR Images Using an Adaptive Trilateral Filter." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/52299551885875669031.

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碩士
國立陽明大學
醫學工程研究所
99
Postacquisition denoising of magnetic resonance(MR) images is of importance for clinical diagnosis and quantitative analysis, such as tissue classification, segmentation, and registration. It is well known that the noise in MR magnitude images obeys a Rician distribution, which not only causes random fluctuations, but also introduces a signal-dependent bias to the data that reduces image contrast . As a consequence, separating signal from noise in those images is particularly difficult. We propose a post-acquisition denoising algorithm called adaptive trilateral filtering to adequately and adaptively remove the random fluctuations and bias introduced by Rician noise. The proposed filter consists of geometric, radiometric, and median-metric components that replaces the intensity value with an weighted average between neighboring pixels associated with an entropy function. In addition, a parameter automation mechanism is proposed to reduce the burden of laborious interventions through a fuzzy membership function, which adaptively responses to local intensity difference. The experimental results indicate that the adaptive trilateral filter outperformed several existing methods in providing greater Rician noise reduction and clearer structure boundaries both quantitatively and qualitatively.
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Li, Cheng-Yuan, and 李正淵. "Automatic Brain Magnetic Resonance Image Denoising Using A GPU-Based Trilateral Filter." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/49218573032509405566.

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碩士
國立臺灣大學
工程科學及海洋工程學研究所
104
Noise removal is one of the fundamental and essential tasks within image processing. In medical imaging, finding an effective algorithm that can remove random noise in magnetic resonance (MR) images is important. This thesis proposes an effective noise reduction method for brain MR images. The proposed is based on the trilateral filter, which is a more powerful method than the bilateral filter in many cases. However, the computation of the trilateral filter is quite time-consuming and the choice of the filter parameters is also laborious. To address these problems, the trilateral filter algorithm is implemented using parallel computing with GPU. The CUDA, an application programming interface for GPU by NVIDIA is adopted, to accelerate the computation. Subsequently, the optimal filter parameters are selected by artificial intelligence techniques. Artificial neural networks and support vector machines associated with image feature analysis are proposed to establish the automatic mechanism. The best feature combination is selected by the t-test and the sequential forward floating selection (SFFS) methods. Experimental results indicated that not only did the proposed GPU-based version run dramatically faster than the traditional trilateral filter, but this automatic system also effectively removed the noise in various brain MR images. We believe that the proposed framework has established a general blueprint for achieving fast and automatic filtering in a wide variety of MR image denoising applications.
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Books on the topic "TRILATERAL FILTER"

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Kalantzakos, Sophia. How China Came to Dominate the Rare Earth Industry. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190670931.003.0005.

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Once a leader in the production and trading of rare earths, the United States relinquished the reins to China in the 1990s. The People’s Republic of China declared rare earths “protected and strategic materials” and proceeded to control production and processing, introduced export quotas, and sought to dominate the supply chain for crucial applications. It also made investments in mines worldwide. The 2010 crisis caused a parabolic rise in prices, leading the United States, the European Union, and Japan to file a complaint against China at the World Trade Organization, in 2012, and to launch trilateral cooperation workshops, starting in 2011, to promote recycling, substitution, and innovation. China lost its WTO appeal and removed the export quotas in May 2015. The market corrected itself, and it may seem today that China lost an initial battle; but closer examination indicates that it may not have lost the war.
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Book chapters on the topic "TRILATERAL FILTER"

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Calderon, Felix, and Mariano Rivera. "One Trilateral Filter Based on Surface Normal." In Advances in Artificial Intelligence, 301–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16761-4_27.

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Tripathi, Purvi, Richard Obler, Andreas Maier, and Hendrik Janssen. "A Novel Trilateral Filter for Digital Subtraction Angiography." In Bildverarbeitung für die Medizin 2021, 310–15. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33198-6_75.

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Zhang, Shengqian, Wei Zhong, Long Ye, and Qin Zhang. "A Modified Joint Trilateral Filter for Depth Image Super Resolution." In Communications in Computer and Information Science, 53–62. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4211-9_6.

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Zhang, Ju, and Yun Cheng. "Despeckling Method for Medical Images Based on Wavelet and Trilateral Filter." In Despeckling Methods for Medical Ultrasound Images, 103–22. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0516-4_5.

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Begill, Arun, and Sankalap Arora. "An Improved DCT Based Image Fusion Using Saturation Weighting and Joint Trilateral Filter." In Advances in Intelligent Systems and Computing, 447–57. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23036-8_39.

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Kala, R., and P. Deepa. "Analysis of Rician Noise Restoration Using Fuzzy Membership Function with Median and Trilateral Filter in MRI." In Lecture Notes in Electrical Engineering, 803–16. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5558-9_69.

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Conference papers on the topic "TRILATERAL FILTER"

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Yu, Yongjian, Gang Dong, and Jue Wang. "Despeckling trilateral filter." In 2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis. IEEE, 2011. http://dx.doi.org/10.1109/ivmspw.2011.5970352.

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Onuki, Masaki, and Yuichi Tanaka. "Trilateral filter on graph spectral domain." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025410.

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Chang, Ting-An, Kuan-Ting Lee, Guan-Cheng Chen, Shu-Hui Chiu, and Jar-Ferr Yang. "Super resolution using trilateral filter regression interpolation." In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP). IEEE, 2017. http://dx.doi.org/10.1109/siprocess.2017.8124511.

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Kesireddy, Akitha, and Mohamed El-Sharkawy. "Adaptive Trilateral Filter for In-Loop Filtering." In Third International conference on Advanced Computer Science and Information Technology. AIRCC Publishing Corporation, 2014. http://dx.doi.org/10.5121/csit.2014.4705.

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Kim, Jaekwang, Jaeho Lee, Seungryong Han, Dowan Kim, Jongsul Min, and Changick Kim. "Trilateral filter construction for depth map upsampling." In 2013 11th IVMSP Workshop: 3D Image/Video Technologies and Applications. IEEE, 2013. http://dx.doi.org/10.1109/ivmspw.2013.6611911.

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Gao, Kai, Yan Piao, and Jing-he Zhang. "An iterative trilateral filter algorithm for depth map." In Selected Proceedings of the Photoelectronic Technology Committee Conferences held August-October 2014, edited by Xun Hou, Zhihong Wang, Lingan Wu, and Jing Ma. SPIE, 2015. http://dx.doi.org/10.1117/12.2175482.

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Zhang, Zhenbo, Yihua Xuan, Yajie Wei, Qixin Ge, and Liguo Han. "Seismic data deblending based on the trilateral filter." In SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17751909.1.

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Isono, Daiki, Xiaohua Zhang, and Ning Xie. "Scale adaptive structure tensor based rolling trilateral filter." In International Workshop on Advanced Imaging Technology (IWAIT 2022), edited by Shogo Muramatsu, Masayuki Nakajima, Jae-Gon Kim, Jing-Ming Guo, and Qian Kemao. SPIE, 2022. http://dx.doi.org/10.1117/12.2624212.

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Jager, F., and J. Balle. "Median trilateral loop filter for depth map video coding." In 2012 Picture Coding Symposium (PCS). IEEE, 2012. http://dx.doi.org/10.1109/pcs.2012.6213286.

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Chen, Dongming, Mohsen Ardabilian, and Liming Chen. "Depth edge based trilateral filter method for stereo matching." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351208.

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