TY - GEN
T1 - Improved sparse coding using manifold projections
AU - Ramamurthy, Karthikeyan Natesan
AU - Thiagarajan, Jayaraman J.
AU - Spanias, Andreas
PY - 2011
Y1 - 2011
N2 - Sparse representations using predefined and learned dictionaries have widespread applications in signal and image processing. Sparse approximation techniques can be used to recover data from its low dimensional corrupted observations, based on the knowledge that the data is sparsely representable using a known dictionary. In this paper, we propose a method to improve data recovery by ensuring that the data recovered using sparse approximation is close its manifold. This is achieved by performing regularization using examples from the data manifold. This technique is particularly useful when the observations are highly reduced in dimensions when compared to the data and corrupted with high noise. Using an example application of image inpainting, we demonstrate that the proposed algorithm achieves a reduction in reconstruction error in comparison to using only sparse coding with predefined and learned dictionaries, when the percentage of missing pixels is high.
AB - Sparse representations using predefined and learned dictionaries have widespread applications in signal and image processing. Sparse approximation techniques can be used to recover data from its low dimensional corrupted observations, based on the knowledge that the data is sparsely representable using a known dictionary. In this paper, we propose a method to improve data recovery by ensuring that the data recovered using sparse approximation is close its manifold. This is achieved by performing regularization using examples from the data manifold. This technique is particularly useful when the observations are highly reduced in dimensions when compared to the data and corrupted with high noise. Using an example application of image inpainting, we demonstrate that the proposed algorithm achieves a reduction in reconstruction error in comparison to using only sparse coding with predefined and learned dictionaries, when the percentage of missing pixels is high.
KW - dictionary learning
KW - image inpainting
KW - manifold projection
KW - sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84856283162&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84856283162&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2011.6115656
DO - 10.1109/ICIP.2011.6115656
M3 - Conference contribution
AN - SCOPUS:84856283162
SN - 9781457713033
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1237
EP - 1240
BT - ICIP 2011
T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011
Y2 - 11 September 2011 through 14 September 2011
ER -