Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context

TL;DR

This paper introduces a multi-kernel filtering approach inspired by human vision to adaptively denoise images with non-stationary noise, outperforming existing methods by using hierarchical image context for kernel adaptation.

Contribution

The paper presents a novel multi-kernel filter that automatically adapts to image variations using hierarchical clustering and image context, extending bilateral filtering with spatially-varying kernels.

Findings

Outperforms state-of-the-art filters in denoising accuracy

Effective on images with integrally-varying and spatially-varying noise

Demonstrates superior structural similarity and mean absolute error metrics

Abstract

Bilateral filtering (BF) is one of the most classical denoising filters, however, the manually initialized filtering kernel hampers its adaptivity across images with various characteristics. To deal with image variation (i.e., non-stationary noise), in this paper, we propose multi-kernel filter (MKF) which adapts filtering kernels to specific image characteristics automatically. The design of MKF takes inspiration from adaptive mechanisms of human vision that make full use of information in a visual context. More specifically, for simulating the visual context and its adaptive function, we construct the image context based on which we simulate the contextual impact on filtering kernels. We first design a hierarchically clustering algorithm to generate a hierarchy of large to small coherent image patches, organized as a cluster tree, so that obtain multi-scale image representation. The leaf cluster and corresponding predecessor clusters are used to generate one of multiple range kernels that are capable of catering to image variation. At first, we design a hierarchically clustering framework to generate a hierarchy of large to small coherent image patches that organized as a cluster tree, so that obtain multi-scale image representation, i.e., the image context. Next, a leaf cluster is used to generate one of the multiple kernels, and two corresponding predecessor clusters are used to fine-tune the adopted kernel. Ultimately, the single spatially-invariant kernel in BF becomes multiple spatially-varying ones. We evaluate MKF on two public datasets, BSD300 and BrainWeb which are added integrally-varying noise and spatially-varying noise, respectively. Extensive experiments show that MKF outperforms state-of-the-art filters w.r.t. both mean absolute error and structural similarity.