TY - GEN
T1 - Local learning-based image super-resolution
AU - Lu, Xiaoqiang
AU - Yuan, Haoliang
AU - Yuan, Yuan
AU - Yan, Pingkun
AU - Li, Luoqing
AU - Li, Xuelong
PY - 2011
Y1 - 2011
N2 - Local learning algorithm has been widely used in single-frame super-resolution reconstruction algorithm, such as neighbor embedding algorithm [1] and locality preserving constraints algorithm [2]. Neighbor embedding algorithm is based on manifold assumption, which defines that the embedded neighbor patches are contained in a single manifold. While manifold assumption does not always hold. In this paper, we present a novel local learning-based image single-frame SR reconstruction algorithm with kernel ridge regression (KRR). Firstly, Gabor filter is adopted to extract texture information from low-resolution patches as the feature. Secondly, each input low-resolution feature patch utilizes K nearest neighbor algorithm to generate a local structure. Finally, KRR is employed to learn a map from input low-resolution (LR) feature patches to high-resolution (HR) feature patches in the corresponding local structure. Experimental results show the effectiveness of our method.
AB - Local learning algorithm has been widely used in single-frame super-resolution reconstruction algorithm, such as neighbor embedding algorithm [1] and locality preserving constraints algorithm [2]. Neighbor embedding algorithm is based on manifold assumption, which defines that the embedded neighbor patches are contained in a single manifold. While manifold assumption does not always hold. In this paper, we present a novel local learning-based image single-frame SR reconstruction algorithm with kernel ridge regression (KRR). Firstly, Gabor filter is adopted to extract texture information from low-resolution patches as the feature. Secondly, each input low-resolution feature patch utilizes K nearest neighbor algorithm to generate a local structure. Finally, KRR is employed to learn a map from input low-resolution (LR) feature patches to high-resolution (HR) feature patches in the corresponding local structure. Experimental results show the effectiveness of our method.
UR - http://www.scopus.com/inward/record.url?scp=84055176668&partnerID=8YFLogxK
U2 - 10.1109/MMSP.2011.6093843
DO - 10.1109/MMSP.2011.6093843
M3 - 会议稿件
AN - SCOPUS:84055176668
SN - 9781457714337
T3 - MMSP 2011 - IEEE International Workshop on Multimedia Signal Processing
BT - MMSP 2011 - IEEE International Workshop on Multimedia Signal Processing
T2 - 3rd IEEE International Workshop on Multimedia Signal Processing, MMSP 2011
Y2 - 17 November 2011 through 19 November 2011
ER -