Fast Approximation to Large-Kernel Edge-Preserving Filters by Recursive Reconstruction from Image Pyramids
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Edge-preserving filters, as known as bilateral filters, are fundamental to graphics rendering techniques, providing greater generality and capability of edge preservation than pure convolution filters. However, sampling with a large kernel per pixel for these filters can be computationally intensive in real-time rendering. Existing acceleration methods for approximating edgepreserving filters still struggle to balance blur controllability, edge clarity, and runtime efficiency. In this paper, we propose a novel scheme for approximating edge-preserving filters with large anisotropic kernels by recursively reconstructing them from multi-image pyramid (MIP) layers that are weightedly filtered in a dual 3×3 kernel space. Our approach introduces a concise unified processing pipeline independent of kernel size, which includes upsampling and downsampling on MIP layers and enables the integration of custom edge-stopping functions. We also derive the implicit relations of the sampling weights and formulate a weight template model for inference. Furthermore, we convert the pipeline into a lightweight neural network for numerical solutions through data training. Consequently, our image post-processors achieve high-quality and high-performance edgepreserving filters in real-time, using the same control parameters as the original bilateral filters. These filters are applicable for depth-of-fields, global illumination denoising, and screen-space particle rendering. The simplicity of the reconstruction process in our pipeline makes it user-friendly and cost-effective, saving both runtime and implementation costs.
Description
CCS Concepts: Computing methodologies → Rendering; Visibility
@inproceedings{10.2312:pg.20241302,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{Fast Approximation to Large-Kernel Edge-Preserving Filters by Recursive Reconstruction from Image Pyramids}},
author = {Xu, Tianchen and Yang, Jiale and Qin, Yiming and Sheng, Bin and Wu, Enhua},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241302}
}