Perspective Texture Synthesis Based on Improved Energy Optimization

TL;DR

This paper introduces an improved energy optimization algorithm for perspective texture synthesis that enhances speed and quality by using patch-based computation, clustering, and PCA, making it more efficient than existing methods.

Contribution

The paper proposes a novel, faster perspective texture synthesis method that combines patch-based energy optimization, k-means clustering, and PCA for improved efficiency and quality.

Findings

Achieves higher quality texture synthesis

Reduces computation time compared to similar methods

Validates effectiveness through high-quality results

Abstract

Perspective texture synthesis has great significance in many fields like video editing, scene capturing etc., due to its ability to read and control global feature information. In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures. Energy optimization technique is a pixel-based approach, so it is time-consuming. We improve it from two aspects with the purpose of achieving faster synthesis and high quality. Firstly, we change this pixel-based technique by replacing the pixel computation with a little patch. Secondly, we present a novel technique to accelerate searching nearest neighborhoods in energy optimization. Using k- means clustering technique to build a search tree to accelerate the search. Hence, we make use of principal component analysis (PCA) technique to reduce dimensions of input vectors. The high quality results prove that our approach is feasible. Besides, our proposed algorithm needs shorter time relative to other similar methods.