Literatura académica sobre el tema "TRILATERAL FILTER"
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Artículos de revistas sobre el tema "TRILATERAL FILTER"
Patanavijit, Vorapoj y 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, n.º 2 (5 de diciembre de 2017): 190–202. http://dx.doi.org/10.37936/ecti-cit.2017112.61688.
Texto completoJayanthi Sree, S. y C. Vasanthanayaki. "De-Speckling of Ultrasound Images Using Local Statistics-Based Trilateral Filter". Journal of Circuits, Systems and Computers 28, n.º 09 (agosto de 2019): 1950150. http://dx.doi.org/10.1142/s0218126619501500.
Texto completoZhang, Xiaohua, Yuelan Xin y Ning Xie. "Anisotropic Joint Trilateral Rolling Filter for Image Smoothing". Journal of the Institute of Industrial Applications Engineers 7, n.º 3 (25 de julio de 2019): 91–98. http://dx.doi.org/10.12792/jiiae.7.91.
Texto completoReddy, N. Sudhir y V. Khanaa. "Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network". International Journal of Experimental Research and Review 31, Spl Volume (30 de julio de 2023): 12–22. http://dx.doi.org/10.52756/10.52756/ijerr.2023.v31spl.002.
Texto completoKesireddy, Akitha y Mohamed El-Sharkawy. "Adaptive Trilateral Filter for HEVC Standard". International journal of Multimedia & Its Applications 6, n.º 4 (31 de agosto de 2014): 19–26. http://dx.doi.org/10.5121/ijma.2014.6402.
Texto completoSerikawa, Seiichi y Huimin Lu. "Underwater image dehazing using joint trilateral filter". Computers & Electrical Engineering 40, n.º 1 (enero de 2014): 41–50. http://dx.doi.org/10.1016/j.compeleceng.2013.10.016.
Texto completoChen, Shuhan, Weiren Shi y 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.
Texto completoWang, Tao. "JPEG2000 image postprocessing with novel trilateral deringing filter". Optical Engineering 47, n.º 2 (1 de febrero de 2008): 027005. http://dx.doi.org/10.1117/1.2844731.
Texto completoHu, Chunyue, Dongyang Li, Zhenyan Sun, Ning Zhang y Jianjun Lei. "Region-based trilateral filter for depth video coding". International Journal of Embedded Systems 11, n.º 2 (2019): 163. http://dx.doi.org/10.1504/ijes.2019.098293.
Texto completoLei, Jianjun, Ning Zhang, Zhenyan Sun, Dongyang Li y Chunyue Hu. "Region-based trilateral filter for depth video coding". International Journal of Embedded Systems 11, n.º 2 (2019): 163. http://dx.doi.org/10.1504/ijes.2019.10019711.
Texto completoTesis sobre el tema "TRILATERAL FILTER"
KAVITA. "IMAGE DENOISING USING MOVING FRAME APPROACH BASED ON TRILATERAL FILTER". Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16523.
Texto completoKesireddy, Akitha. "A new adaptive trilateral filter for in-loop filtering". Thesis, 2014. http://hdl.handle.net/1805/5927.
Texto completoHEVC 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.
Shu-HuiChiu y 邱淑惠. "Super Resolution Using Trilateral Filter Regression with Local Texture Enhancement". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/24fj2y.
Texto completo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.
Tai, Hung-Shou y 戴宏碩. "The Hardware Architecture Design of Trilateral Noise Filter for Color Images". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/59667062748549662497.
Texto completo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.
Hsieh, Tung-Ju y 謝東儒. "Rician Noise Removal in MR Images Using an Adaptive Trilateral Filter". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/52299551885875669031.
Texto completo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.
Li, Cheng-Yuan y 李正淵. "Automatic Brain Magnetic Resonance Image Denoising Using A GPU-Based Trilateral Filter". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/49218573032509405566.
Texto completo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.
Libros sobre el tema "TRILATERAL FILTER"
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.
Texto completoCapítulos de libros sobre el tema "TRILATERAL FILTER"
Calderon, Felix y Mariano Rivera. "One Trilateral Filter Based on Surface Normal". En Advances in Artificial Intelligence, 301–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16761-4_27.
Texto completoTripathi, Purvi, Richard Obler, Andreas Maier y Hendrik Janssen. "A Novel Trilateral Filter for Digital Subtraction Angiography". En Bildverarbeitung für die Medizin 2021, 310–15. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33198-6_75.
Texto completoZhang, Shengqian, Wei Zhong, Long Ye y Qin Zhang. "A Modified Joint Trilateral Filter for Depth Image Super Resolution". En Communications in Computer and Information Science, 53–62. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4211-9_6.
Texto completoZhang, Ju y Yun Cheng. "Despeckling Method for Medical Images Based on Wavelet and Trilateral Filter". En Despeckling Methods for Medical Ultrasound Images, 103–22. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0516-4_5.
Texto completoBegill, Arun y Sankalap Arora. "An Improved DCT Based Image Fusion Using Saturation Weighting and Joint Trilateral Filter". En 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.
Texto completoKala, R. y P. Deepa. "Analysis of Rician Noise Restoration Using Fuzzy Membership Function with Median and Trilateral Filter in MRI". En Lecture Notes in Electrical Engineering, 803–16. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5558-9_69.
Texto completoActas de conferencias sobre el tema "TRILATERAL FILTER"
Yu, Yongjian, Gang Dong y Jue Wang. "Despeckling trilateral filter". En 2011 IEEE 10th IVMSP Workshop: Perception and Visual Signal Analysis. IEEE, 2011. http://dx.doi.org/10.1109/ivmspw.2011.5970352.
Texto completoOnuki, Masaki y Yuichi Tanaka. "Trilateral filter on graph spectral domain". En 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025410.
Texto completoChang, Ting-An, Kuan-Ting Lee, Guan-Cheng Chen, Shu-Hui Chiu y Jar-Ferr Yang. "Super resolution using trilateral filter regression interpolation". En 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP). IEEE, 2017. http://dx.doi.org/10.1109/siprocess.2017.8124511.
Texto completoKesireddy, Akitha y Mohamed El-Sharkawy. "Adaptive Trilateral Filter for In-Loop Filtering". En Third International conference on Advanced Computer Science and Information Technology. AIRCC Publishing Corporation, 2014. http://dx.doi.org/10.5121/csit.2014.4705.
Texto completoKim, Jaekwang, Jaeho Lee, Seungryong Han, Dowan Kim, Jongsul Min y Changick Kim. "Trilateral filter construction for depth map upsampling". En 2013 11th IVMSP Workshop: 3D Image/Video Technologies and Applications. IEEE, 2013. http://dx.doi.org/10.1109/ivmspw.2013.6611911.
Texto completoGao, Kai, Yan Piao y Jing-he Zhang. "An iterative trilateral filter algorithm for depth map". En Selected Proceedings of the Photoelectronic Technology Committee Conferences held August-October 2014, editado por Xun Hou, Zhihong Wang, Lingan Wu y Jing Ma. SPIE, 2015. http://dx.doi.org/10.1117/12.2175482.
Texto completoZhang, Zhenbo, Yihua Xuan, Yajie Wei, Qixin Ge y Liguo Han. "Seismic data deblending based on the trilateral filter". En SEG Technical Program Expanded Abstracts 2017. Society of Exploration Geophysicists, 2017. http://dx.doi.org/10.1190/segam2017-17751909.1.
Texto completoIsono, Daiki, Xiaohua Zhang y Ning Xie. "Scale adaptive structure tensor based rolling trilateral filter". En International Workshop on Advanced Imaging Technology (IWAIT 2022), editado por Shogo Muramatsu, Masayuki Nakajima, Jae-Gon Kim, Jing-Ming Guo y Qian Kemao. SPIE, 2022. http://dx.doi.org/10.1117/12.2624212.
Texto completoJager, F. y J. Balle. "Median trilateral loop filter for depth map video coding". En 2012 Picture Coding Symposium (PCS). IEEE, 2012. http://dx.doi.org/10.1109/pcs.2012.6213286.
Texto completoChen, Dongming, Mohsen Ardabilian y Liming Chen. "Depth edge based trilateral filter method for stereo matching". En 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7351208.
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