Singular value decomposition with weighting matrix applied for optical-resolution photoacoustic microscopes.

The prestige target selectivity and imaging depth of optical-resolution photoacoustic microscope (OR-PAM) have gained attentions to enable advanced intra-cellular visualizations. However, the broad-band nature of photoacoustic signals is prone to noise and artifacts caused by the inefficient light-to-pressure translation, resulting in poor image quality. The present study foresees application of singular value decomposition (SVD) to effectively extract the photoacoustic signals from these noise and artifacts.

Utilization of Curvelet Transform in Reconstructing Cellular Images for Undersampled Optical-Resolution Photoacoustic Microscopy.

We address the problem of limited temporal resolution in optical-resolution microscopy (OR-PAM) for cellular imaging by undersampling and reconstruction. A curvelet transform method in a compressed sensing framework (CS-CVT) was devised to specifically reconstruct the boundary and separability of cell objects in an image. The performance of the CS-CVT approach was justified by comparisons with the natural neighbor interpolation (NNI) followed by smoothing filters on various imaging objects.