Single-shot wide-field topography measurement using spectrally multiplexed reflection intensity holography via space-domain Kramers-Kronig relations.

Surface topology measurements of micro- or nanostructures are essential for both scientific and industrial applications. However, high-throughput measurements remain challenging in surface metrology. We present single-shot full-field surface topography measurement using Kramers-Kronig holographic imaging and spectral multiplexing. Three different intensity images at different incident angles were simultaneously measured with three different colors, from which a quantitative phase image was retrieved using spatial Kramers-Kronig relations. A high-resolution topographic image of the sample was then reconstructed using synthetic aperture holography. Various patterned structures at the nanometer scale were measured and cross-validated using atomic force microscopy.

Pupil-aberration calibration with controlled illumination for quantitative phase imaging.

Quantitative phase imaging (QPI) exploits sample-induced changes in the optical field to analyze biological specimens in a label-free manner. However, the quantitative nature of QPI makes it susceptible to optical aberrations. We propose a method for calibrating pupil aberrations by imaging a sample of interest. The proposed method recovers pupil information by utilizing the cross-spectral density between optical fields at different incident angles and allows both thin and weakly scattering three-dimensional samples for calibration. We experimentally validate the proposed method by imaging various samples, including a resolution target, breast tissue, and a polystyrene bead, and demonstrate aberration-free two- and three-dimensional QPI.

Low-coherence optical diffraction tomography using a ferroelectric liquid crystal spatial light modulator.

Optical diffraction tomography (ODT) is a three-dimensional (3D) label-free imaging technique. The 3D refractive index distribution of a sample can be reconstructed from multiple two-dimensional optical field images via ODT. Herein, we introduce a temporally low-coherence ODT technique using a ferroelectric liquid crystal spatial light modulator (FLC SLM). The fast binary-phase modulation provided by the FLC SLM ensures the high spatiotemporal resolution. To reduce coherent noise, a superluminescent light-emitting diode is used as an economic low-coherence light source. We demonstrate the performance of the proposed system using various samples, including colloidal microspheres and live epithelial cells.

Speckle-Correlation Scattering Matrix Approaches for Imaging and Sensing through Turbidity.

The development of optical and computational techniques has enabled imaging without the need for traditional optical imaging systems. Modern lensless imaging techniques overcome several restrictions imposed by lenses, while preserving or even surpassing the capability of lens-based imaging. However, existing lensless methods often rely on a priori information about objects or imaging conditions. Thus, they are not ideal for general imaging purposes. The recent development of the speckle-correlation scattering matrix (SSM) techniques facilitates new opportunities for lensless imaging and sensing. In this review, we present the fundamentals of SSM methods and highlight recent implementations for holographic imaging, microscopy, optical mode demultiplexing, and quantification of the degree of the coherence of light. We conclude with a discussion of the potential of SSM and future research directions.

White-light quantitative phase imaging unit: erratum.

We found an error in Fig. 1 of our article "White-light Quantitative Phase Imaging Unit." Here we publish the revised figure.

White-light quantitative phase imaging unit.

We introduce the white-light quantitative phase imaging unit (WQPIU) as a practical realization of quantitative phase imaging (QPI) on standard microscope platforms. The WQPIU is a compact stand-alone unit which measures sample induced phase delay under white-light illumination. It does not require any modification of the microscope or additional accessories for its use. The principle of the WQPIU based on lateral shearing interferometry and phase shifting interferometry provides a cost-effective and user-friendly use of QPI. The validity and capacity of the presented method are demonstrated by measuring quantitative phase images of polystyrene beads, human red blood cells, HeLa cells and mouse white blood cells. With speckle-free imaging capability due to the use of white-light illumination, the WQPIU is expected to expand the scope of QPI in biological sciences as a powerful but simple imaging tool.

DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning.

Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization algorithms that use a priori information, such as non-negativity and sample smoothness. However, the iterative nature of these algorithms and their parameter dependency make real-time visualization impossible. In this article, we propose and experimentally demonstrate a deep neural network, which we term DeepRegularizer, that rapidly improves the resolution of a three-dimensional refractive index map. Trained with pairs of datasets (a raw refractive index tomogram and a resolution-enhanced refractive index tomogram via the iterative total variation algorithm), the three-dimensional U-net-based convolutional neural network learns a transformation between the two tomogram domains. The feasibility and generalizability of our network are demonstrated using bacterial cells and a human leukaemic cell line, and by validating the model across different samples. DeepRegularizer offers more than an order of magnitude faster regularization performance compared to the conventional iterative method. We envision that the proposed data-driven approach can bypass the high time complexity of various image reconstructions in other imaging modalities.