The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Makalah ini menunjukkan bahawa terdapat dunia yang bermanfaat di sebalik teorem pensampelan. Untuk tujuan ini, masalah pensampelan dirumuskan semula dari sudut analisis fungsional, dan akibatnya mendedahkan bahawa masalah pensampelan adalah sejenis masalah songsang. Masalah pensampelan meliputi, sebagai contoh, pemulihan isyarat dan imej termasuk resolusi super, pembinaan semula imej daripada unjuran seperti pengimbas CT di hospital, dan pembelajaran diselia seperti pembelajaran dalam rangkaian saraf tiruan. Pengendali pembinaan semula yang optimum juga diberikan, memberikan anggaran terbaik kepada isyarat asal individu tanpa kita mengetahui isyarat asal.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Hidemitsu OGAWA, "What Can We See behind Sampling Theorems?" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 3, pp. 688-695, March 2009, doi: 10.1587/transfun.E92.A.688.
Abstract: This paper shows that there is a fruitful world behind sampling theorems. For this purpose, the sampling problem is reformulated from a functional analytic standpoint, and is consequently revealed that the sampling problem is a kind of inverse problem. The sampling problem covers, for example, signal and image restoration including super resolution, image reconstruction from projections such as CT scanners in hospitals, and supervised learning such as learning in artificial neural networks. An optimal reconstruction operator is also given, providing the best approximation to an individual original signal without our knowing the original signal.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.688/_p
Salinan
@ARTICLE{e92-a_3_688,
author={Hidemitsu OGAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={What Can We See behind Sampling Theorems?},
year={2009},
volume={E92-A},
number={3},
pages={688-695},
abstract={This paper shows that there is a fruitful world behind sampling theorems. For this purpose, the sampling problem is reformulated from a functional analytic standpoint, and is consequently revealed that the sampling problem is a kind of inverse problem. The sampling problem covers, for example, signal and image restoration including super resolution, image reconstruction from projections such as CT scanners in hospitals, and supervised learning such as learning in artificial neural networks. An optimal reconstruction operator is also given, providing the best approximation to an individual original signal without our knowing the original signal.},
keywords={},
doi={10.1587/transfun.E92.A.688},
ISSN={1745-1337},
month={March},}
Salinan
TY - JOUR
TI - What Can We See behind Sampling Theorems?
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 688
EP - 695
AU - Hidemitsu OGAWA
PY - 2009
DO - 10.1587/transfun.E92.A.688
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2009
AB - This paper shows that there is a fruitful world behind sampling theorems. For this purpose, the sampling problem is reformulated from a functional analytic standpoint, and is consequently revealed that the sampling problem is a kind of inverse problem. The sampling problem covers, for example, signal and image restoration including super resolution, image reconstruction from projections such as CT scanners in hospitals, and supervised learning such as learning in artificial neural networks. An optimal reconstruction operator is also given, providing the best approximation to an individual original signal without our knowing the original signal.
ER -