Conjugate Gradient Acceleration of Non-Linear Smoothing Filters

TL;DR

This paper introduces a conjugate gradient method to accelerate non-linear edge-preserving smoothing filters like bilateral and guided filters without tuning, significantly reducing computation time while maintaining quality.

Contribution

It presents a novel conjugate gradient-based approach to speed up non-linear smoothing filters without parameter tuning, addressing a key efficiency challenge.

Findings

20x acceleration of bilateral filter

3-5x acceleration of guided filter

Maintains filter quality during acceleration

Abstract

The most efficient signal edge-preserving smoothing filters, e.g., for denoising, are non-linear. Thus, their acceleration is challenging and is often performed in practice by tuning filter parameters, such as by increasing the width of the local smoothing neighborhood, resulting in more aggressive smoothing of a single sweep at the cost of increased edge blurring. We propose an alternative technology, accelerating the original filters without tuning, by running them through a special conjugate gradient method, not affecting their quality. The filter non-linearity is dealt with by careful freezing and restarting. Our initial numerical experiments on toy one-dimensional signals demonstrate 20x acceleration of the classical bilateral filter and 3-5x acceleration of the recently developed guided filter.