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×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 hardwareefficient 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 stateof-the-art results in terms of accuracy and efficiency in the
computation intensive multi-label systems.