Description: 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
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SparseLab200-Core\Papers
.................\......\Contents.m
.................\......\HDCPNPD
.................\......\.......\HDCPNPD.pdf
.................\......\.......\HDCPNPDDemo
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.................\......\.......\...........\..........\shannon.m
.................\......\.......\...........\..........\SeriesMillsSolve.m
.................\......\.......\...........\..........\yFroms.m
.................\......\.......\...........\..........\zeta1.m
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.................\......\.......\...........\..........\CalcXnu.m
.................\......\.......\...........\..........\GenRhoNDiff.m
.................\......\.......\...........\..........\Contents.m
.................\......\.......\...........\HDCPNPDPath.m
.................\......\.......\...........\Contents.m
.................\......\.......\...........\HDCPNPDDemo.m
.................\......\.......\...........\HDCPNPDFig.m
.................\......\.......\...........\HDCPNPDInit.m
.................\......\.......\...........\HDCPNPDIntro.m
.................\......\.......\Contents.m
.................\......\NPSSULE
.................\......\.......\NPSSULE.pdf
.................\......\.......\NPSSULEDemo
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.................\......\.......\...........\shannon.m
.................\......\.......\...........\CalcXnu.m
.................\......\.......\...........\GenRhoNDiff.m
.................\......\.......\...........\NPSSULETable1.m
.................\......\.......\...........\PsiCom.m
.................\......\.......\...........\PsiExt.m
.................\......\.......\...........\PsiInt.m
.................\......\.......\...........\PsiNet.m
.................\......\.......\...........\SeriesMillsSolve.m
.................\......\.......\...........\PsiSect.m
.................\......\.......\...........\PsiFace.m
.................\......\.......\...........\NPSSULErhoF.mat
.................\......\.......\Contents.m
.................\......\NRPSHD
.................\......\......\NRPSHD.pdf
.................\......\......\NRPSHDDemo
.................\......\......\..........\GenFigures
.................\......\......\..........\..........\CalcXnu.m
.................\......\......\..........\..........\NRPSHDFig4.m
.................\......\......\..........\..........\NRPSHDFig5.m
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.................\......\......\..........\..........\PsiCom.m
.................\......\......\..........\..........\PsiExt.m
.................\......\......\..........\..........\PsiInt.m
.................\......\......\..........\..........\SeriesMillsSolve.m
.................\......\......\..........\..........\NRPSHDFig3.m
.................\......\......\..........\..........\NRPSHDFig2.m
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