Hardware-Efficient Guided Image Filtering For Multi-Label Problem

TL;DR

This paper introduces a hardware-efficient guided filter (HGF) that significantly improves multichannel image filtering speed and accuracy for multi-label problems by utilizing a novel matrix inverse algorithm and polynomial guidance.

Contribution

The paper proposes a new hardware-efficient guided filter that replaces traditional matrix inversion with element-wise and box filtering operations, enabling faster multichannel filtering.

Findings

HGF achieves state-of-the-art speed in multichannel filtering.

HGF improves accuracy in multi-label image processing.

The polynomial guidance enhances sensitivity to image structure.

Abstract

The Guided Filter (GF) is well-known for its linear complexity. However, when filtering an image with an n-channel guidance, GF needs to invert an n x n matrix for each pixel. To the best of our knowledge existing matrix inverse algorithms are inefficient on current hardwares. This shortcoming limits applications of multichannel guidance in computation intensive system such as multi-label system. We need a new GF-like filter that can perform fast multichannel image guided filtering. Since the optimal linear complexity of GF cannot be minimized further, the only way thus is to bring all potentialities of current parallel computing hardwares into full play. In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance. Specifically, in order to boost the filtering performance, HGF takes a new matrix inverse algorithm which only involves two hardware-efficient operations: element-wise arithmetic calculations and box filtering. In order to break the linear model restriction, HGF synthesizes a polynomial multichannel guidance to introduce nonlinearity. Benefiting from our polynomial guidance and hardware-efficient matrix inverse algorithm, HGF not only is more sensitive to the underlying structure of guidance but also achieves the fastest computing speed. Due to these merits, HGF obtains state-of-the-art results in terms of accuracy and efficiency in the computation intensive multi-label