Jun Tanida

3D Imaging with Compressive Sensing

In this invited paper, we present an overview of our previously published work on using compressive sensing in 3D imaging. We shall examine a variety of 3D imaging approaches and applications, including integral imaging, holography, 3D scene reconstruction and 3D object recognition.

Learning-based signal retrieval from scattering media

Machine learning is an efficient tool to estimate input signals from the output ones for nonlinear systems. In case of object observation through scattering media with coherent illumination, the output signals could be speckle patterns highly disordered from the input ones. Even for the case, machine learning is effective to retrieve the input information. In the method, a number of pairs of input and output signals are used for training, and then untrained input signals can be retrieved from the observed output signals. We demonstrated object recognition, imaging, and focusing through scattering media and confirmed effectiveness of the presented method.

Lateral spatial resolution improvement in laser scanning fluorescence microscopy using a subdiffraction limit optical spot

This paper presents lateral spatial resolution improvement by scanning a focused spot array pattern. To enhance the effective point spread function (PSF), we employed a phase-only computer generated hologram (CGH) that can reduce the spot size comparing to the single diffraction limited spot. A CGH was designed based on the Gerchberg-Saxton algorithm with a specific constraint to control the dispersion of light energy and the phase of generated spots. As a design example, we obtained a CGH for generating 3x3 optical spots whose sizes were reduced to 78.5% of the single diffraction limited spot. We also confirmed by simulation that the effective PSF was improved from 208 nm to 183 nm when using the subdiffraction limit spots for excitation.