tailieunhanh - Báo cáo sinh học: " Adaptive example-based super-resolution using Kernel PCA with a novel classification approach"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Adaptive example-based super-resolution using Kernel PCA with a novel classification approach | EURASIP Journal on Advances in Signal Processing SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. Adaptive example-based super-resolution using Kernel PCA with a novel classification approach EURASIP Journal on Advances in Signal Processing 2011 2011 138 doi 1687-6180-2011-138 Takahiro Ogawa ogawa@ Miki Haseyama miki@ ISSN 1687-6180 Article type Research Submission date 8 June 2011 Acceptance date 22 December 2011 Publication date 22 December 2011 Article URL http content 2011 1 138 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in EURASIP Journal on Advances in Signal Processing go to http authors instructions For information about other SpringerOpen publications go to http 2011 Ogawa and Haseyama licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Adaptive example-based super-resolution using Kernel PCA with a novel classification approach Takahiro Ogawa 1 and Miki Haseyama1 1Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Corresponding author ogawa@ E-mail address MH miki@ Abstract An adaptive example-based super-resolution SR using kernel principal component analysis PCA with a novel classification approach is presented in this paper. In order to enable estimation of missing high-frequency components for each kind of texture in target low-resolution LR images