Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting

TL;DR

This paper introduces a space-filling curve-based multi-index acceleration structure that significantly speeds up exemplar-based image inpainting, especially for large datasets, while maintaining compatibility with various inpainting methods.

Contribution

The authors propose a novel multi-index scheme using space-filling curves and dimensionality reduction to drastically accelerate patch search in exemplar-based inpainting.

Findings

Achieves up to 660x speedup over traditional methods

Theoretic runtime of O(log^2 n) per iteration

Compatible with most model-based inpainting algorithms

Abstract

Exemplar-based inpainting is the process of reconstructing missing parts of an image by searching the remaining data for patches that fit seamlessly. The image is completed to a plausible-looking solution by repeatedly inserting the patch that is the best match according to some cost function. We present an acceleration structure that uses a multi-index scheme to accelerate this search procedure drastically, particularly in the case of very large datasets. The index scheme uses ideas such as dimensionality reduction and k-nearest neighbor search on space-filling curves that are well known in the field of multimedia databases. Our method has a theoretic runtime of O(log2 n) per iteration and reaches a speedup factor of up to 660 over the original method. The approach has the advantage of being agnostic to most modelbased parts of exemplar-based inpainting such as the order in which patches are processed and the cost function used to determine patch similarity. Thus, the acceleration structure can be used in conjunction with most exemplar-based inpainting algorithms.