Determining spatial resolution from images is crucial when optimizing focus, determining smallest resolvable object, and assessing size measurement uncertainties. However, no standard algorithm exists to measure resolution from electron microscopy (EM) images, though several have been proposed, where most require user decisions. We present the Spatial Image Resolution Assessment by Fourier analysis (SIRAF) algorithm that uses fast Fourier transform analysis to estimate resolution directly from a single image without user inputs. The method is derived from the underlying assumption that objects display intensity transitions, resembling a step function blurred by a Gaussian point spread function. This hypothesis is tested and verified on simulated EM images with known resolution. To identify potential pitfalls, the algorithm is also tested on simulated images with a variety of settings, and on real SEM images acquired at different magnification and defocus settings. Finally, the versatility of the method is investigated by assessing resolution in images from several microscopy techniques. It is concluded that the algorithm can assess resolution from a large selection of image types, thereby providing a measure of this fundamental image parameter. It may also improve autofocus methods and guide the optimization of magnification settings when balancing spatial resolution and field of view.

The wideband coherent-pulse radar provides high-resolution image of the target. The model of this image is a complex envelope superposition corresponding to signals diffracted by the point scatterers. The values of complex envelopes are distributed over the radar image coordinate plane in accordance with the point scatterer positions and their reflection coefficients. The radar image model consists of range and Doppler profiles. The parameters of the target point scatterers were defined by processing of two-dimensional (2D) data extracted from the complex 2D discrete Fourier transforms of the radar image. The proposed parametric system identification method performs the estimation of the model parameters for a short dwell time and the extrapolation of the radar data image beyond this time. The modified procedure of inverse synthesis aperture radar imaging applied to actual data showed a reduction of the Doppler smearing and some improvements of image resolution.

Scanning transmission electron microscopy in scanning electron microscopy (STEM-in-SEM) is a convenient technique for soft materials characterization. Various specimen-holder geometries and detector arrangements have been used for bright-field (BF) STEM-in-SEM imaging. In this study, to further the characterization potential of STEM-IN-SEM, a new specimen holder has been developed to facilitate direct detection of BF signals and indirect detection of dark-field (DF) signals without the need for substantial instrument modification. DF imaging is conducted with the use of a gold (Au)-coated copper (Cu) plate attached to the specimen holder which directs highly scattered transmitted electrons to an off-axis yttrium-aluminum-garnet (YAG) detector. A hole in the copper plate allows for BF imaging with a transmission electron (TE) detector. The inclusion of an Au-coated Cu plate enhanced DF signal intensity. Experiments validating the acquisition of true DF signals revealed that atomic number (Z) contrast may be achieved for materials with large lattice spacing. However, materials with small lattice spacing still exhibit diffraction contrast effects in this approach. The calculated theoretical fine probe size is 1.8 nm. At 30 kV, in this indirect approach, DF spatial resolution is limited to 3.2 nm as confirmed experimentally.

The aberration-corrected scanning transmission electron microscope has great sensitivity to environmental or instrumental disturbances such as acoustic, mechanical, or electromagnetic interference. This interference can introduce distortions to the images recorded and degrade both signal noise and resolution performance. In addition, sample or stage drift can cause the images to appear warped and leads to unreliable lattice parameters being exhibited. Here a detailed study of the sources, natures, and effects of imaging distortions is presented, and from this analysis a piece of image reconstruction code has been developed that can restore the majority of the effects of these detrimental image distortions for atomic-resolution data. Example data are presented, and the performance of the restored images is compared quantitatively against the as-recorded data. An improvement in apparent resolution of 16% and an improvement in signal-to-noise ratio of 30% were achieved, as well as correction of the drift up to the precision to which it can be measured.