An improved image analogy method based on adaptive CUDA-accelerated neighborhood matching framework
The image analogy framework is especially useful to synthesize appealing images for non-homogeneous input and gives users creative control over the synthesized results. However, the traditional framework did not adaptively employ the searching strategy based on neighborhoods different textural contents. Besides, the synthesis speed is slow due to intensive computation involved in neighborhood matching. In this paper we present a CUDA-based neighborhood matching algorithm for image analogy. Our algorithm adaptively applies the global search of the exact L 2 nearest neighbor and k-coherence search strategies during synthesis according to different textural features of images, which is especially usefully for non-homogeneous textures. To consistently implement the above two search strategies on GPU, we adopt the fast k nearest neighbor searching algorithm based on CUDA. Such an acceleration greatly reduces the time of the pre-process of k-coherence search and the synthesis procedure of the global search, which makes possible the adjustment of important synthesis parameters. We further adopt synthesis magnification to get the final high-resolution synthesis image for running efficiency. Experimental results show that our algorithm is suitable for various applications of the image analogy framework and takes full advantage of GPUs parallel processing capability to improve synthesis speed and get satisfactory synthesis results.
image analogy cuda synthesis magnification texture synthesis parallel optimization