Bidirectional Texture Function (BTF) is one of the methods to reproduce realistic images in Computer Graphics (CG). This is a technique that can be applied to texture mapping with changing lighting and viewing directions and can reproduce realistic appearance by a simple and high-speed
processing. However, in the BTF method, a large amount of texture data is generally measured and stored in advance. In this paper, in order to address the problems related to the measurement time and the texture data size in the BTF reproduction, we a method to generate a BTF image dataset
using deep learning. We recovery texture images under various azimuth lighting conditions from a single texture image. For achieving this goal, we applied the U-Net to our BTF recovery. The restored and original texture images are compared using SSIM. It will be confirmed that the reproducibility
of fabric and wood textures is high.