Unsharp Mask Guided Filtering

TL;DR

This paper introduces a simplified guided filtering method inspired by unsharp masking, estimating a single coefficient for structure transfer, leading to improved results and efficiency in various image filtering tasks.

Contribution

A novel guided filtering formulation that estimates only one coefficient, reducing artifacts and enabling a more efficient successive filtering network.

Findings

Achieves state-of-the-art results in image upsampling, denoising, and cross-modality filtering.

Reduces halo artifacts and structure inconsistencies compared to previous methods.

Provides a flexible trade-off between accuracy and efficiency through multiple filtering outputs.

Abstract

The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering by means of an additional guidance image. Where classical guided filters transfer structures using hand-designed functions, recent guided filters have been considerably advanced through parametric learning of deep networks. The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter. In this work, we posit that simultaneously estimating both coefficients is suboptimal, resulting in halo artifacts and structure inconsistencies. Inspired by unsharp masking, a classical technique for edge enhancement that requires only a single coefficient, we propose a new and simplified formulation of the guided filter. Our formulation enjoys a filtering prior from a low-pass filter and enables explicit structure transfer by estimating a single coefficient. Based on our proposed formulation, we introduce a successive guided filtering network, which provides multiple filtering results from a single network, allowing for a trade-off between accuracy and efficiency. Extensive ablations, comparisons and analysis show the effectiveness and efficiency of our formulation and network, resulting in state-of-the-art results across filtering tasks like upsampling, denoising, and cross-modality filtering. Code is available at \url{https://github.com/shizenglin/Unsharp-Mask-Guided-Filtering}.