Currently, there is an increasing demand for wooden furniture and products, especially in the decorating business where wood textures are widely used. These textures have different artificial designs, and because different consumers have different needs for wood texture, the trend of using computer algorithms to design wood textures emerged. We propose a method for synthesizing wood’s heterogeneous texture. It can analyze the characteristics of different wood textures, select the most appropriate input sample block size, and then generate a new image with a sample texture. Compared with the deep learning method, our method reduces pressure on system resources and production costs. The proposed method also generates higher quality reconstructed images than traditional algorithms.