Directional Bilateral Filters

TL;DR

This paper introduces a directional bilateral filter that incorporates local orientation information via an oriented Gaussian domain kernel, improving edge-preserving smoothing and denoising performance over traditional bilateral filters.

Contribution

The paper presents a novel directional bilateral filter with a locally controlled domain kernel based on structure tensors, and optimizes parameters using Stein's unbiased risk estimate.

Findings

Outperforms Gaussian bilateral filter in PSNR across noise levels

Effective in suppressing outliers while smoothing

Validated on synthetic and real endoscopic images

Abstract

We propose a bilateral filter with a locally controlled domain kernel for directional edge-preserving smoothing. Traditional bilateral filters use a range kernel, which is responsible for edge preservation, and a fixed domain kernel that performs smoothing. Our intuition is that orientation and anisotropy of image structures should be incorporated into the domain kernel while smoothing. For this purpose, we employ an oriented Gaussian domain kernel locally controlled by a structure tensor. The oriented domain kernel combined with a range kernel forms the directional bilateral filter. The two kernels assist each other in effectively suppressing the influence of the outliers while smoothing. To find the optimal parameters of the directional bilateral filter, we propose the use of Stein's unbiased risk estimate (SURE). We test the capabilities of the kernels separately as well as together, first on synthetic images, and then on real endoscopic images. The directional bilateral filter has better denoising performance than the Gaussian bilateral filter at various noise levels in terms of peak signal-to-noise ratio (PSNR).