TY - GEN
T1 - Comparison of dictionary learning methods for reverberation suppression in photoacoustic microscopy
T2 - 53rd Annual Conference on Information Sciences and Systems, CISS 2019
AU - Sathyanarayana, Sushanth G.
AU - Ning, Bo
AU - Hu, Song
AU - Hossack, John A.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/16
Y1 - 2019/4/16
N2 - Dictionary learning is an unsupervised learning method to abstract image data into a set of learned basis vectors. In prior work, the efficacy of the K-SVD dictionary learning algorithm in suppressing reverberation in volumetric photoacoustic microscopy (PAM) data was demonstrated. In this work, we compare the K-SVD algorithm against the method of optimal directions (MOD). The generalization error and reverberation suppression performance of the two algorithms were compared. The K-SVD was found to have a lower average generalization error (5.69x104 ±9.09x103 (a.u.)) when compared to the MOD (8.27x104 ±1.33x104 (a.u.)) for identical training data, initialization, sparsity (3 atoms per A-line) and number of iterations (5). Both algorithms were observed to suppress the reverberation to a similar extent (18.8 ± 1.12 dB for the K-SVD and 18.3 ± 1.2 dB for the MOD). Our data show that irrespective of the method used, sparse dictionary learning can significantly suppress reverberations in PAM.
AB - Dictionary learning is an unsupervised learning method to abstract image data into a set of learned basis vectors. In prior work, the efficacy of the K-SVD dictionary learning algorithm in suppressing reverberation in volumetric photoacoustic microscopy (PAM) data was demonstrated. In this work, we compare the K-SVD algorithm against the method of optimal directions (MOD). The generalization error and reverberation suppression performance of the two algorithms were compared. The K-SVD was found to have a lower average generalization error (5.69x104 ±9.09x103 (a.u.)) when compared to the MOD (8.27x104 ±1.33x104 (a.u.)) for identical training data, initialization, sparsity (3 atoms per A-line) and number of iterations (5). Both algorithms were observed to suppress the reverberation to a similar extent (18.8 ± 1.12 dB for the K-SVD and 18.3 ± 1.2 dB for the MOD). Our data show that irrespective of the method used, sparse dictionary learning can significantly suppress reverberations in PAM.
KW - dictionary learning
KW - K-SVD
KW - MOD
KW - Photoacoustic microscopy
KW - reverberation
UR - http://www.scopus.com/inward/record.url?scp=85065191358&partnerID=8YFLogxK
U2 - 10.1109/CISS.2019.8693042
DO - 10.1109/CISS.2019.8693042
M3 - Conference contribution
AN - SCOPUS:85065191358
T3 - 2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
BT - 2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 March 2019 through 22 March 2019
ER -