Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution

TL;DR

This paper introduces a fast, GPU-optimized Green Function Convolution method for solving Laplacian equations in gradient-domain image editing, significantly outperforming existing techniques in speed and accuracy.

Contribution

The paper presents a novel Green Function Convolution approach that enables rapid, minimal-error solutions to Laplacian equations for gradient domain editing, adaptable to various image dimensions and hardware.

Findings

Solves 100 Laplacian problems in 1 ms for 801x1200 images.

Outperforms existing methods by orders of magnitude in speed.

Proves mathematically and empirically that the method is the least error solver.

Abstract

In computer vision, the gradient and Laplacian of an image are used in different applications, such as edge detection, feature extraction, and seamless image cloning. Computing the gradient of an image is straightforward since numerical derivatives are available in most computer vision toolboxes. However, the reverse problem is more difficult, since computing an image from its gradient requires to solve the Laplacian equation, also called Poisson equation. Current discrete methods are either slow or require heavy parallel computing. The objective of this paper is to present a novel fast and robust method of solving the image gradient or Laplacian with minimal error, which can be used for gradient domain editing. By using a single convolution based on a numerical Green's function, the whole process is faster and straightforward to implement with different computer vision libraries. It can also be optimized on a GPU using fast Fourier transforms and can easily be generalized for an n dimension image. The tests show that, for images of resolution 801x1200, the proposed GFC can solve 100 Laplacian in parallel in around 1.0 milliseconds ms. This is orders of magnitude faster than our nearest competitor which requires 294ms for a single image. Furthermore, we prove mathematically and demonstrate empirically that the proposed method is the least error solver for gradient domain editing. The developed method is also validated with examples of Poisson blending, gradient removal, and the proposed gradient domain merging GDM. Finally, we present how the GDM can be leveraged in future works for convolutional neural networks CNN.