Comparison of dictionary learning methods for reverberation suppression in photoacoustic microscopy : Invited presentation

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.