Perspective Texture Synthesis Based on Improved Energy Optimization
This work addresses efficiency issues in perspective texture synthesis for applications like video editing and scene capturing, but it is incremental as it builds on existing energy optimization techniques.
The paper tackles the problem of slow perspective texture synthesis by improving an energy optimization-based algorithm, achieving faster synthesis and high quality results with reduced computational time compared to similar methods.
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.