high performance computing on graphics processing units: hgpu.org

The framework of Texture-by-numbers (TBN) synthesizes images of global-varying patterns with intuitive user control. Previous TBN synthesis methods have difficulties in achieving high-quality synthesis results and efficiency simultaneously. This paper proposes a fast TBN synthesis method based on texture optimization, which uses global optimization to solve the controllable non-homogeneous texture synthesis problem. Our algorithm produces high quality synthesis results by combining texture optimization into TBN framework with two improvements. The initialization process is adopted to generate the initial output of the global optimization algorithm, which speeds up the algorithm’s convergence rate and ensures synthesis quality. Besides different metrics to measure image similarity are defined to match human visual perception better. To further improve the synthesis speed, the algorithm is entirely implemented on GPU based on CUDA architecture. The experimental results show that this method synthesizes realistic images with high efficiency, which is not only applicable to the traditional TBN application, but also suitable for other applications including nonphotorealistic rendering and image in-painting.