Title: A GPU-based statistical image up-sampling method by using edge templates

Authors: Xin Zheng; Chenlei Lv; Qingqing Xu; Peipei Pan; Ping Guo

Addresses: College of Information Science and Technology, Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China ' College of Information Science and Technology, Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China ' College of Information Science and Technology, Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China ' College of Information Science and Technology, Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China ' College of Information Science and Technology, Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China

Abstract: 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.

Keywords: compute unified device architecture; CUDA; image up-sampling; Markov mode; super-resolution; image interpolation; GPU; graphical processing units; edge templates; image processing; image quality.

DOI: 10.1504/IJCSE.2017.081169

International Journal of Computational Science and Engineering, 2017 Vol.14 No.1, pp.64 - 73

Received: 26 Apr 2013
Accepted: 10 Dec 2013
Published online: 26 Dec 2016 *

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