Gaussian Fourier Pyramid for Local Laplacian Filter

TL;DR

This paper introduces Fourier LLF, an improved local Laplacian filter using Fourier series expansion, which enhances accuracy and adaptiveness in multi-scale image processing while maintaining computational efficiency.

Contribution

The paper proposes Fourier LLF, a novel approximation method that improves accuracy and adaptiveness over traditional local Laplacian filtering using Fourier series expansion.

Findings

Fourier LLF achieves higher accuracy than existing methods.

Fourier LLF is parameter-adaptive for content-specific filtering.

Fourier LLF maintains efficiency comparable to previous approximations.

Abstract

Multi-scale processing is essential in image processing and computer graphics. Halos are a central issue in multi-scale processing. Several edge-preserving decompositions resolve halos, e.g., local Laplacian filtering (LLF), by extending the Laplacian pyramid to have an edge-preserving property. Its processing is costly; thus, an approximated acceleration of fast LLF was proposed to linearly interpolate multiple Laplacian pyramids. This paper further improves the accuracy by Fourier series expansion, named Fourier LLF. Our results showed that Fourier LLF has a higher accuracy for the same number of pyramids. Moreover, Fourier LLF exhibits parameter-adaptive property for content-adaptive filtering. The code is available at: https://norishigefukushima.github.io/GaussianFourierPyramid/.