Scanning Acoustic Microscopy Image Super-Resolution using Bilateral Weighted Total Variation Regularization.

Scanning acoustic microscopy (SAM) is an imaging modality used to obtain 2D maps of acoustical and mechanical properties of soft tissues and uses ultrasound transducers operating at very high-frequencies. Such transducers are challenging and costly to manufacture, and SAM systems at higher frequencies become more sensitive to experimental issues. Nevertheless, biomedical applications of SAM often require spatial resolutions nearly as good as light microscopy. In addition, stained histology photomicrographs of thin sections of tissues are easily obtained with the necessary resolution and accuracy. Consequently, the aim of this study is to introduce a bilateral approach that enhances the resolution of SAM images by leveraging the co-registered high-resolution histology image. We propose to use bilateral weighted total variation regularization to solve the super-resolution problem. A fast matrix-less solver is developed by utilizing the Alternating Direction Method of Multipliers (ADMM) and solving the least squares problem in one ADMM step in the Fourier domain. Reconstruction results on experimentally recorded SAM and histology data show promising improvement over the classical techniques.