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Relevant bibliographies by topics / TRILATERAL FILTER

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Journal articles

Academic literature on the topic 'TRILATERAL FILTER'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "TRILATERAL FILTER"

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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