Quantitative Evaluation of Transform Domains for Compressive Sampling-Based Recovery of Sparsely Sampled Volumetric OCT Images

Recently, compressive sampling has received significant attention as an emerging technique for rapid volumetric imaging. We have previously investigated volumetric optical coherence tomography (OCT) image acquisition using compressive sampling techniques and showed that it was possible to recover image volumes from a subset of sampled images. Our previous findings used the multidimensional wavelet transform as the domain of sparsification for recovering OCT image volumes. In this report, we analyzed and compared the potential and efficiency of three other image transforms to reconstruct the same volumetric OCT image. Two quantitative measures, the mean square error and the structural similarity index, were applied to compare the quality of the reconstructed volumetric images. We observed that fast Fourier transformation and wavelet both are capable of reconstructing OCT image volumes for the orthogonal sparse sampling masks used in this report, but with different merits.