Improving the depth resolution of STEM-ADF sectioning by 3D deconvolution.

Although the possibility of locating single atom in three dimensions using the scanning transmission electron microscope (STEM) has been discussed with the advent of aberration correction technology, it is still a big challenge. In this report we have developed deconvolution routines based on maximum entropy method (MEM) and Richardson-Lucy algorithm (RLA), which are applicable to the STEM-annular dark-field (ADF) though-focus images to improve the depth resolution. The new three-dimensional (3D) deconvolution routines require a limited defocus-range of STEM-ADF images that covers a whole sample and some vacuum regions. Since the STEM-ADF probe is infinitely elongated along the optical axis, a 3D convolution is performed with a two-dimensional (2D) convolution over xy-plane using the 2D fast Fourier transform in reciprocal space, and a one-dimensional convolution along the z-direction in real space. Using our new deconvolution routines, we have processed simulated focal series of STEM-ADF images for single Ce dopants embedded in wurtzite-type AlN. Applying the MEM, the Ce peaks are clearly localized along the depth, and the peak width is reduced down to almost one half. We also applied the new deconvolution routines to experimental focal series of STEM-ADF images of a monolayer graphene. The RLA gives smooth and high-P/B ratio scattering distribution, and the graphene layer can be easily detected. Using our deconvolution algorithms, we can determine the depth locations of the heavy dopants and the graphene layer within the precision of 0.1 and 0.2 nm, respectively. Thus, the deconvolution must be extremely useful for the optical sectioning with 3D STEM-ADF images.

Simultaneous atomic-resolution electron ptychography and Z-contrast imaging of light and heavy elements in complex nanostructures.

The aberration-corrected scanning transmission electron microscope (STEM) has emerged as a key tool for atomic resolution characterization of materials, allowing the use of imaging modes such as Z-contrast and spectroscopic mapping. The STEM has not been regarded as optimal for the phase-contrast imaging necessary for efficient imaging of light materials. Here, recent developments in fast electron detectors and data processing capability is shown to enable electron ptychography, to extend the capability of the STEM by allowing quantitative phase images to be formed simultaneously with incoherent signals. We demonstrate this capability as a practical tool for imaging complex structures containing light and heavy elements, and use it to solve the structure of a beam-sensitive carbon nanostructure. The contrast of the phase image contrast is maximized through the post-acquisition correction of lens aberrations. The compensation of defocus aberrations is also used for the measurement of three-dimensional sample information through post-acquisition optical sectioning.

A diagnosis support system for capsule endoscopy.

The diagnostic time required for a full, 8-hour video capsule endoscopy is usually between 45 and 120 min. The aim of this work is to evaluate the diagnostic time required when applying a method that adaptively controlls the image display rate. The advantage of the method is that the sequence can be played at high speed in stable smooth sequences to save time and then decreased at sequences where there are sudden rough changes, in order to assess suspicious findings detail. In this paper, this method is examined under real conditions: 10 sequences were independently evaluated by 4 medical doctors. The methods of evaluation include: 1) the time required for reading a sequence, 2) the percentage of abnormal regions accurately found, and 3) the manipulations of the evaluating physicians. The results indicate that the proposed method reduces diagnostic time to around 10 +/- 1.5% length of the sequence and is of valuable assistance to medical doctors.

Adaptively merging large-scale range data with reflectance properties.

In this paper, we tackle the problem of geometric and photometric modeling of large intricately shaped objects. Typical target objects we consider are cultural heritage objects. When constructing models of such objects, we are faced with several important issues that have not been addressed in the past-issues that mainly arise due to the large amount of data that has to be handled. We propose two novel approaches to efficiently handle such large amounts of data: A highly adaptive algorithm for merging range images and an adaptive nearest-neighbor search to be used with the algorithm. We construct an integrated mesh model of the target object in adaptive resolution, taking into account the geometric and/or photometric attributes associated with the range images. We use surface curvature for the geometric attributes and (laser) reflectance values for the photometric attributes. This adaptive merging framework leads to a significant reduction in the necessary amount of computational resources. Furthermore, the resulting adaptive mesh models can be of great use for applications such as texture mapping, as we will briefly demonstrate. Additionally, we propose an additional test for the k-d tree nearest-neighbor search algorithm. Our approach successfully omits back-tracking, which is controlled adaptively depending on the distance to the nearest neighbor. Since the main consumption of computational cost lies in the nearest-neighbor search, the proposed algorithm leads to a significant speed-up of the whole merging process. In this paper, we present the theories and algorithms of our approaches with pseudo code and apply them to several real objects, including large-scale cultural assets.