Summary:A new smoothing filter has been developed for noise removal of scanning electron microscopy (SEM) images. We call this the complex hysteresis smoothing (CHS) filter. It is much easier to use for SEM operators than any other conventional smoothing filter, and it rarely produces processing artifacts because it does not utilize a definite mask (which usually has processing parameters of size, shape, weight, and the number of iterations) like a common averaging filter or a complicated filter shape in the Fourier domain. Its criterion for distinguishing noise depends simply on the amplitude of the SEM signal. When applied to several images with different characteristics, it is shown that the present method has a high performance with some original advantages.

This study proposes an efficient and fast method of scanning (e.g., television (TV) scan) coupled with digital image processing technology to replace the conventional slow-scan mode as a standard model of acquisition for general-purpose scanning electron microscopy (SEM). SEM images obtained using the proposed method had the same quality in terms of sharpness and noise as slow-scan images, and it was able to suppress the adverse effects of charging in a full-vacuum condition, which is a challenging problem in this area. Two problems needed to be solved in designing the proposed method. One was suitable compensation in image quality using the inverse filter based on characteristics of the frequency of a TV-scan image, and the other to devise an accurate technique of image integration (noise suppression), the position alignment of which is robust against noise. This involved using the image montage technique and estimating the number of images needed for the integration. The final result of our TV-scan mode was compared with the slow-scan image as well as the conventional TV-scan image.

Certain digital image-processing methods, which are useful for nonperiodic structural images, have been applied to high-resolution SEM images for the improvement of resolution. Samples utilized in the present study consisted of magnetic tape coated with gold, T4 phage coated with goldpalladium, and uncoated specimens of Prolamellar body (PLB) in Cucurbita moschata. These images were blurred and otherwise disturbed by electronic noise, though the images were taken at the limit of efficiency of intrinsic instrument. The major image-processing tool was the Laplacian filter, which subtracts the Laplacian from the original image. Noise, which is a serious problem in digital processing of high-resolution SEM images, was suppressed by the nonlinear type smoothing method. Also, the noise was evaluated by an autocorrelation function and a power spectrum of the image. By using these methods of "deblurring" and noise removal, we achieved better resolution, and structural details of our biological specimens were revealed.Digital image processing, Laplacin filter, Scanning electron

An effective combination of the low voltage and variable pressure (VP) scanning electron microscopy (SEM) are discussed. In low voltage VP-SEM, helium gas is utilized for reducing the amount of scatter of the primary electron beam. Most samples can receive various benefits obtained from the combination of low voltage and low vacuum observation. Compared to a back-scattered electron (BSE) image in air, signal-to-noise ratio (SNR) of a BSE image taken with helium gas is 5.4 times under a pressure of 50 Pa and an accelerating voltage of 1.5 kV.

Quality of an SEM image is strongly influenced by the extent of noise. As a well-known method in the field of SEM, the covariance is applied to measure the signal-to-noise ratio (SNR). This method has potential ability for highly accurate measurement of the SNR, which is hardly known until now. If the precautions discussed in this article are adopted, that method can demonstrate its real ability. These precautions are strongly related to "proper acquisition of two images with the identical view," "alignment of an aperture diaphragm," "reduction of charging phenomena," "elimination of particular noises," and "accurate focusing," As necessary, characteristics in SEM signal and noise are investigated from a few standpoints. When using the maximum performance of this measurement, SNR of many SEM images obtained in a variety of the SEM operating conditions and specimens can be measured accurately.