Learning kernel parameter lookup tables to implement adaptive bilateral filtering

Abstract

Bilateral filtering is a widely used image smoothing filter that preserves image edges while also smoothing texture. In previous research, the focus of improving the bilateral filter has primarily been on constructing an adaptive range kernel. However, recent research has shown that even slight noise perturbations can prevent the bilateral filter from effectively preserving image edges. To address this issue, we employ a neural network to learn the kernel parameters that can effectively counteract noise perturbations. Additionally, to enhance the adaptability of the learned kernel parameters to the local edge features of the image, we utilize the edge-sensitive indexing method to construct kernel parameter lookup tables (LUTs). During testing, we determine the appropriate spatial kernel and range kernel parameters for each pixel using a lookup table and interpolation. This allows us to effectively smooth the image in the presence of noise perturbation. In this paper, we conducted comparative experiments on several datasets to verify that the proposed method outperforms existing bilateral filtering methods in preserving image structure, removing image texture, and resisting slight noise perturbations. The code is available at https://github.com/FightingSrain/AdaBFLUT.