@inproceedings{b95422b4c5814dcfb8dfb42ddfa15eca,
title = "High-resolution reconstruction of human brain MRI image based on local polynomial regression",
abstract = "This paper introduces a new local polynomial regression (LPR)-based high-resolution image reconstruction method for human brain magnetic resonance images. In LPR, the image pixels are modeled locally by a polynomial using least-squares (LS) criterion with a kernel having a certain bandwidth matrix. Steering kernels with local orientation are used in LPR to adapt better to local characteristics of images. Furthermore, a refined intersection of confidence intervals (RICI) adaptive scale selector is adopted to select the scale of the steering kernels. The resulting steering-kernel-based LPR with RICI (SK-LPR-RICI) method is applied to reconstruct a high-resolution brain MRI image from a set of low-resolution MRI images. Simulation results show that the proposed SK-LPR-RICI method can effectively improve the image resolution and peak signal-to-noise ratio.",
keywords = "Adaptive scale selection, Image reconstruction, Local polynomial regression, MRI",
author = "Zhang, {Z. G.} and Chan, {S. C.} and X. Zhang and Lam, {E. Y.} and Wu, {E. X.} and Y. Hu",
year = "2009",
doi = "10.1109/NER.2009.5109279",
language = "English (US)",
isbn = "9781424420735",
series = "2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09",
pages = "245--248",
booktitle = "2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09",
note = "2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09 ; Conference date: 29-04-2009 Through 02-05-2009",
}