SparseLab200-Core
所属分类：图形图像处理
开发工具：matlab
文件大小：26949KB
下载次数：262
上传日期：2010-11-07 11:15:03
上 传 者：
try0311@sina.com
说明： 基于多帧图像插值（Interpolation）技术的方法是SR恢复技术当中最直观
的方法。这类方法首先估计各帧图像之间的相对运动信息，获得HR图像在非均
匀间距采样点上的象素值，接着通过非均匀插值得到HR栅格上的象素值，最后
采用图像恢复技术来去除模糊和降低噪声（运动估计!非均匀插值!去模糊和
噪声）。
(In this paper, we propose a novel method for solv-
ing single-image super-resolution problems. Given a
low-resolution image as input, we recover its high-
resolution counterpart using a set of training exam-
ples. While this formulation resembles other learning-
based methods for super-resolution, our method has
been inspired by recent manifold learning methods, par-
ticularly locally linear embedding (LLE). Speci?cally,
small image patches in the low- and high-resolution
images form manifolds with similar local geometry in
two distinct feature spaces. As in LLE, local geometry
is characterized by how a feature vector correspond-
ing to a patch can be reconstructed by its neighbors
in the feature space. Besides using the training image
pairs to estimate the high-resolution embedding, we
also enforce local compatibility and smoothness con-
straints between patches in the target high-resolution
image through overlapping. Experiments show that our
method is very ?exible )
文件列表:
SparseLab200-Core\Papers (0, 2006-03-28)
SparseLab200-Core\Papers\Contents.m (1693, 2007-03-24)
SparseLab200-Core\Papers\HDCPNPD (0, 2006-03-28)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPD.pdf (460600, 2006-02-10)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo (0, 2006-03-28)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures (0, 2006-03-28)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig1.m (696, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig3.m (507, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig4.m (500, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig5.m (696, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig6.m (746, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig7.m (737, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig8.m (364, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig9.m (421, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\PsiCom.m (533, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\PsiExt.m (428, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\PsiInt.m (620, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig10.m (548, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig11.m (628, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\PsiFace.m (467, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\PsiNet.m (705, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\PsiSect.m (445, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\shannon.m (399, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\SeriesMillsSolve.m (1419, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\yFroms.m (361, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\zeta1.m (688, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\MillsM.m (538, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\HDCPNPDFig2.m (3069, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\CalcXnu.m (847, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\GenRhoNDiff.m (347, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\GenFigures\Contents.m (2116, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\HDCPNPDPath.m (417, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\Contents.m (1037, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\HDCPNPDDemo.m (11760, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\HDCPNPDFig.m (1268, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\HDCPNPDInit.m (250, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\HDCPNPDDemo\HDCPNPDIntro.m (250, 2006-07-30)
SparseLab200-Core\Papers\HDCPNPD\Contents.m (923, 2006-07-30)
SparseLab200-Core\Papers\NPSSULE (0, 2006-03-28)
SparseLab200-Core\Papers\NPSSULE\NPSSULE.pdf (231000, 2006-02-10)
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