In reconstruction based super-resolution, it is an important issue to up-sample and to merge the high-frequency information contained in low-resolution images efficiently and without artifact. The conventional up-sampling methods, which used for registering low-resolution data to high-resolution grid, have the difference between them and ideal upsampling in observation modeling. In this paper, we analyze the difference and propose a new up-sampling and merging method which is able to incorporate low-resolution images without loss of the high frequency data and minimizes the artifact caused by data insufficiency. By this method, regions that the registered high frequency data do not cover are naturally regularized without using complex regularizers. The experimental results show that the choice of up-sampling significantly affects the quality of resulting images and the proposed up-sampling method gives better results compared with conventional simple up-sampling method.

Date Published: 19 February 2013
PDF: 8 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550S (19 February 2013); doi: 10.1117/12.2003294

Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)