research-article

Parallel recursive filtering of infinite input extensions

Authors:

André MaximoAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 35, Issue 6

Article No.: 204, Pages 1 - 13

https://doi.org/10.1145/2980179.2980222

Published: 05 December 2016 Publication History

Abstract

Filters with slowly decaying impulse responses have many uses in computer graphics. Recursive filters are often the fastest option for such cases. In this paper, we derive closed-form formulas for computing the exact initial feedbacks needed for recursive filtering infinite input extensions. We provide formulas for the constant-padding (e.g. clamp-to-edge), periodic (repeat) and even-periodic (mirror or reflect) extensions. These formulas were designed for easy integration into modern block-parallel recursive filtering algorithms. Our new modified algorithms are state-of-the-art, filtering images faster even than previous methods that ignore boundary conditions.

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Cited By

Kanetaka YTakagi HMaeda YFukushima N(2024)SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for HalideIEEE Access10.1109/ACCESS.2023.334566012(7563-7583)Online publication date: 2024
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Ferreira HGastal E(2023)Efficient 2D Tikhonov smoothness regularization with recursive filteringPattern Recognition Letters10.1016/j.patrec.2023.07.001175(95-103)Online publication date: Nov-2023
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Index Terms

Parallel recursive filtering of infinite input extensions
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Image processing

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 35, Issue 6

November 2016

1045 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2980179

Issue’s Table of Contents

Copyright © 2016 ACM.

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Publication History

Published: 05 December 2016

Published in TOG Volume 35, Issue 6

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Cited By

Kanetaka YTakagi HMaeda YFukushima N(2024)SlidingConv: Domain-Specific Description of Sliding Discrete Cosine Transform Convolution for HalideIEEE Access10.1109/ACCESS.2023.334566012(7563-7583)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3345660
Zhang JChen CChen KJu MZhang D(2023)Local Adaptive Image Filtering Based on Recursive Dilation SegmentationSensors10.3390/s2313577623:13(5776)Online publication date: 21-Jun-2023
https://doi.org/10.3390/s23135776
Ferreira HGastal E(2023)Efficient 2D Tikhonov smoothness regularization with recursive filteringPattern Recognition Letters10.1016/j.patrec.2023.07.001175(95-103)Online publication date: Nov-2023
https://doi.org/10.1016/j.patrec.2023.07.001
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https://dl.acm.org/doi/10.1155/2022/3819409
Ferreira HGastal E(2022)Recursive filtering 2D Tikhonov regularization2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI55357.2022.9991783(168-173)Online publication date: 24-Oct-2022
https://doi.org/10.1109/SIBGRAPI55357.2022.9991783
Maximo A(2021)GPU efficient 1D and 3D recursive filteringDigital Signal Processing10.1016/j.dsp.2021.103076114(103076)Online publication date: Jul-2021
https://doi.org/10.1016/j.dsp.2021.103076
Schuster KTrettner PKobbelt L(2020)High-Performance Image Filters via Sparse ApproximationsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34061823:2(1-19)Online publication date: 26-Aug-2020
https://dl.acm.org/doi/10.1145/3406182
Yang LLiu SSalvi M(2020)A Survey of Temporal Antialiasing TechniquesComputer Graphics Forum10.1111/cgf.1401839:2(607-621)Online publication date: 13-Jul-2020
https://doi.org/10.1111/cgf.14018
Kersting FGastal E(2020)Domain transform for spherical geometry imagesComputers & Graphics10.1016/j.cag.2020.09.01693(71-83)Online publication date: Dec-2020
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Fukushima NMaeda YKawasaki YNakamura MTsumura TSugimoto KKamata S(2018)Efficient Computational Scheduling of Box and Gaussian FIR Filtering for CPU Microarchitecture2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.23919/APSIPA.2018.8659674(875-879)Online publication date: Nov-2018
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