A GPU-based statistical image up-sampling method by using edge templates. (2017) - Zheng, Xin; Lv, Chenlei; Xu, Qingqing; Pan, Peipei; Guo, Ping
Image up-sampling is very important in different fields, so an appropriate high-quality fast and efficient image up-sampling method is needed. Many interpolation-based up-sampling methods have been proposed by many researchers, but the quality of the resulting images is not satisfactory. The details of these images often cannot be accepted when we use them in many fields. On one hand, some of these methods are very fast, but produce images that are lacking many details and information of the original image; the others can produce high quality images, but the methods are very slow. In this paper, we propose a fast statistical image up-sampling method, and we use GPU to accelerate our up-sampling algorithm. We can obtain high quality images based on reducing the input resolution-grids dependency artefacts. And we can rebuild low resolution images' sharp edges fast and get high-quality up-sampled images in real time. We have applied this method in the multi-resolution texture generation of large scale terrain rendering. Experiments prove that our method can achieve ideal effects in real time.