Fast Multi-Layer Laplacian Enhancement

TL;DR

This paper introduces a fast, efficient image enhancement method based on multi-layer Laplacian operators derived from edge-aware kernels, suitable for mobile devices and various filtering applications.

Contribution

It presents a novel approximation of Laplacian operators that simplifies computation and enables multi-scale detail decomposition for improved image editing capabilities.

Findings

Low computational and memory requirements for mobile devices

Versatile filtering applications including detail enhancement and denoising

Effective multi-scale image decomposition and blending

Abstract

A novel, fast and practical way of enhancing images is introduced in this paper. Our approach builds on Laplacian operators of well-known edge-aware kernels, such as bilateral and nonlocal means, and extends these filter's capabilities to perform more effective and fast image smoothing, sharpening and tone manipulation. We propose an approximation of the Laplacian, which does not require normalization of the kernel weights. Multiple Laplacians of the affinity weights endow our method with progressive detail decomposition of the input image from fine to coarse scale. These image components are blended by a structure mask, which avoids noise/artifact magnification or detail loss in the output image. Contributions of the proposed method to existing image editing tools are: (1) Low computational and memory requirements, making it appropriate for mobile device implementations (e.g. as a finish step in a camera pipeline), (2) A range of filtering applications from detail enhancement to denoising with only a few control parameters, enabling the user to apply a combination of various (and even opposite) filtering effects.