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Filtering: A note on the Use of Nonlinear Filtering in Computer Graphics

Authors:

Mark E. Lee,

Richard A. RednerAuthors Info & Claims

IEEE Computer Graphics and Applications, Volume 10, Issue 3

Pages 23 - 29

https://doi.org/10.1109/38.55149

Published: 01 May 1990 Publication History

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Abstract

The application of nonlinear median and alpha-trimmed mean filters to computer graphics is discussed. The filters are simple to implement, do not require large amounts of CPU time to execute, and can be applied iteratively for better control over the filtering effects. A series of examples shows that the filters eliminate spike noise in a stochastic sampling application without smearing the final image.

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Firmino AFrisvad JJensen H(2023)Denoising-Aware Adaptive Sampling for Monte Carlo Ray TracingACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591537(1-11)Online publication date: 23-Jul-2023
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Ye FZhang G(2022)Real Time Display Method of Complex 3D Model under B / S ArchitectureProceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers10.1145/3544109.3544177(375-379)Online publication date: 14-Apr-2022
https://dl.acm.org/doi/10.1145/3544109.3544177
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Filtering: A note on the Use of Nonlinear Filtering in Computer Graphics
1. Computing methodologies
  1. Artificial intelligence
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cover image IEEE Computer Graphics and Applications

IEEE Computer Graphics and Applications Volume 10, Issue 3

May 1990

91 pages

ISSN:0272-1716

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IEEE Computer Society Press

Washington, DC, United States

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Published: 01 May 1990

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Firmino AFrisvad JJensen H(2023)Denoising-Aware Adaptive Sampling for Monte Carlo Ray TracingACM SIGGRAPH 2023 Conference Proceedings10.1145/3588432.3591537(1-11)Online publication date: 23-Jul-2023
Ye FZhang G(2022)Real Time Display Method of Complex 3D Model under B / S ArchitectureProceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers10.1145/3544109.3544177(375-379)Online publication date: 14-Apr-2022
Jeffery KBalents BGossett CPhillips CRose R(2018)Firefly detection with half buffersProceedings of the 8th Annual Digital Production Symposium10.1145/3233085.3233097(1-5)Online publication date: 11-Aug-2018
Santos JSen POliveira M(2018)A framework for developing and benchmarking sampling and denoising algorithms for Monte Carlo renderingThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-018-1521-y34:6-8(765-778)Online publication date: 1-Jun-2018
Balents BGossett CPhillips CRose R(2018)Proceedings of the 8th Annual Digital Production SymposiumundefinedOnline publication date: 11-Aug-2018
Schied CKaplanyan AWyman CPatney AChaitanya CBurgess JLiu SDachsbacher CLefohn ASalvi MMcGuire MPatney A(2017)Spatiotemporal variance-guided filteringProceedings of High Performance Graphics10.1145/3105762.3105770(1-12)Online publication date: 28-Jul-2017
Chaitanya CKaplanyan ASchied CSalvi MLefohn ANowrouzezahrai DAila T(2017)Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoderACM Transactions on Graphics10.1145/3072959.307360136:4(1-12)Online publication date: 20-Jul-2017
Kalantari NBako SSen P(2015)A machine learning approach for filtering Monte Carlo noiseACM Transactions on Graphics10.1145/276697734:4(1-12)Online publication date: 27-Jul-2015
Delbracio MMusé PBuades AChauvier JPhelps NMorel J(2014)Boosting monte carlo rendering by ray histogram fusionACM Transactions on Graphics10.1145/253270833:1(1-15)Online publication date: 7-Feb-2014
Sen PDarabi S(2012)On filtering the noise from the random parameters in Monte Carlo renderingACM Transactions on Graphics10.1145/2167076.216708331:3(1-15)Online publication date: 31-May-2012
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Kalman Filtering: with Real-Time Applications

Decimated rank order filtering

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