Fast and Provably Accurate Bilateral Filtering

TL;DR

This paper introduces a fast, provably accurate algorithm for approximating the bilateral filter with Gaussian range kernel, significantly reducing computational complexity while maintaining high accuracy, suitable for real-time image processing.

Contribution

The paper proposes a novel approximation algorithm for bilateral filtering that reduces complexity to O(1) per pixel for certain spatial filters, with a detailed accuracy analysis.

Findings

The algorithm achieves high accuracy comparable to the exact bilateral filter.

It significantly reduces computational complexity to O(1) per pixel for box and Gaussian spatial filters.

Numerical results show the method is competitive with state-of-the-art approaches in speed and accuracy.

Abstract

The bilateral filter is a non-linear filter that uses a range filter along with a spatial filter to perform edge-preserving smoothing of images. A direct computation of the bilateral filter requires $O(S)$ operations per pixel, where $S$ is the size of the support of the spatial filter. In this paper, we present a fast and provably accurate algorithm for approximating the bilateral filter when the range kernel is Gaussian. In particular, for box and Gaussian spatial filters, the proposed algorithm can cut down the complexity to $O(1)$ per pixel for any arbitrary $S$. The algorithm has a simple implementation involving $N+1$ spatial filterings, where $N$ is the approximation order. We give a detailed analysis of the filtering accuracy that can be achieved by the proposed approximation in relation to the target bilateral filter. This allows us to to estimate the order $N$ required to obtain a given accuracy. We also present comprehensive numerical results to demonstrate that the proposed algorithm is competitive with state-of-the-art methods in terms of speed and accuracy.