Research Into Super-resolution On 3D Surface Texture

Sharpness is an important parameter in signal processing. Indeed, interpreting an image is determined by the possibility of extracting the information it contains. The sharper an image is, the more details it shows. If it is blurred, it will be hard to interpret it. Thus we are limited by the resolution of the image: if we want to zoom beyond this resolution, the necessary interpolation will generate a blurred image. In fact, it is impossible to create the missing information, which corresponds to the details we cannot see in the original image, and we would like to see in the zoomed one. However, it is conceivable to guess this missing data. This would allow increasing the original resolution of an image: this is called super-resolution.Texture is an important factor to represent the real world. Editing and synthesis of textures always play great roles on computer visualization, image processing, computer graphics and virtual reality.Researchers have made a great progress on super-resolution. Their methods only operate on 2D textures or still images. However, real-world surface textures normally comprise rough surface geometry and various reflectance properties. For example, if one maps a 2D texture on a wall when modeling the real world, there will be no changes when the light angle and view angle change in real-time. So the super-resolution algorithm based on 2D texture cannot reach our purpose of rebuilding the real world, and a new method, which can deal with the 3D surface texture, is demanded.This paper presents an inexpensive method for super-resolution on 3D surface textures. We first introduce algorithms of 2D texture super-resolution. Then we choose Freeman's method as a basis and extend it to the research of 3D surface texture. And we propose a framework on 3D surface texture super-resolution. Based on the framework, we research into the representation and relight technology of 3D surface texture. Then we propose the algorithm of 3D surface texture super-resolution. Finally a set of experiments is carried out and results show the effectiveness of the algorithm.