Automatic construction of single frame super-resolution using Cartesian Genetic Programming
Yusuke Natsui and Tomoharu Nagao: Automatic construction of single frame super-resolution using Cartesian Genetic Programming. Sixth IEEE International Workshop on Computational Intelligence Applications (IWCIA 2013), pp. 149-154, 13 July 2013.
In this paper, we propose a single-frame Super-Resolution (SR) method using Cartesian Genetic Programming (CGP). Our method is to learn relationship of pixel values between high-resolution (HR) image and low-resolution (LR) image using CGP, and we construct a SR rule of generating SR image from a LR input image. A single pixel and its neighbor pixels of the LR input image are set to the inputs of CGP. And then, pixel values of the SR image are obtained from the calculated outputs of CGP. Therefore, the SR image is generated from the LR input image. In addition, multiple CGP can improve the quality of SR image. Because our method is to perform for each pixel independently, our method is suitable to parallel processing. Therefore, in order to reduce computational cost, we use parallel processing with graphics processing unit (GPU). Experimental results show efficient processing is constructed. Our method is little less quality than one conventional work which is the state of the art method on image quality, however to perform overwhelmingly faster than the conventional work. We can construct fast and accurate single-frame super-resolution.
J. van Ouwerkerk, "Image super-resolution survey," Image and Vision Computing, vol. 24, pp. 1039-1052, 2006. http://dx.doi.org/10.1016/j.imavis.2006.02.026
X. Li and M. T. Orchard, "New edge-directed interpolation," IEEE Transactions on Image Processing, vol. 10, pp. 1521-1527, 2001. http://dx.doi.org/10.1109/83.951537
J. Sun, J. Sun, Z. Xu, and H.-Y. Shum, "Image super-resolution using gradient profile prior," pp. 1-8, June.
W. Freeman, T. Jones, and E. Pasztor, "Example-based super-resolution," Computer Graphics and Applications, IEEE, vol. 22, No. 2, pp. 56-65, 2002. http://dx.doi.org/10.1109/38.988747
D. Glasner, S. Bagon, and M. Irani, "Super-resolution from a single image," in ICCV, 2009. [Online]. Available: http://www.wisdom.weizmann.ac.il/vision/SingleImageSR.html http://dx.doi.org/10.1109/ICCV.2009.5459271
J. F. Miller and P. Thomson, "Cartesian genetic programming," Processing of the Third European Conference on Genetic Programming (EuropeGP2000)., vol. 1802, pp. 121-132, 2000.
H. R. S. Z. Wang, A. C. Bovik, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, No. 4, pp. 600-612, Apr. 2004. http://dx.doi.org/10.1109/TIP.2003.819861
T. Nagata and T. Nagao, "Single frame super-resolution using cellular evolutionary neural networks," The Transactions of the Institute of Electronics, Information and Communication Engineers Vol. J95-D, No. 10, Page1859-1868, 2012, (in Japanese).
Nvidia cuda: Compute unified device architecture. [Online]. Available: http://developer.nvidia.com/object/cuda.html
I. Ono, H. Sato, and S. Kobayashi, "A new generation alternation model of genetic algorithms and its assessment," ISAI, Vol. 12, No. 5, pp. 734-744., 1997, (in Japanese).