Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part II: Effect on Image Restoration Quality

2019 ◽  
Vol 25 (05) ◽  
pp. 1183-1194
Author(s):  
Mandy C. Nevins ◽  
Richard K. Hailstone ◽  
Eric Lifshin
Keyword(s):  
Particle Size ◽  
Electron Microscope ◽  
Image Quality ◽  
Scanning Electron Microscope ◽  
Image Restoration ◽  
Point Spread Function ◽  
Background Correction ◽  
Point Spread ◽  
Spread Function ◽  
Scanning Electron

AbstractPoint spread function (PSF) deconvolution is an attractive software-based technique for resolution improvement in the scanning electron microscope (SEM) because it can restore information which has been blurred by challenging operating conditions. In Part 1, we studied a modern PSF determination method for SEM and explored how various parameters affected the method's ability to accurately estimate the PSF. In Part 2, we extend this exploration to PSF deconvolution for image restoration. The parameters include reference particle size, PSF smoothing (K), background correction, and restoration denoising (λ). Image quality was assessed by visual inspection and Fourier analysis. Overall, PSF deconvolution improved image quality. Low λ enhanced image sharpness at the cost of noise, while high λ created smoother restorations with less detail. λ should be chosen to balance feature preservation and denoising based on the application. Reference particle size within ±0.9 nm and K within a reasonable range had little effect on restoration quality. Restorations using background-corrected PSFs had superior quality compared with using no background correction, but if the correction was too high, the PSF was cut off causing blurrier restorations. Future efforts to automatically determine parameters would remove user guesswork, improve this method's consistency, and maximize interpretability of outputs.

Exploring the Parameter Space of Point Spread Function Determination for the Scanning Electron Microscope—Part I: Effect on the Point Spread Function

2019 ◽  
Vol 25 (05) ◽  
pp. 1167-1182
Author(s):  
Mandy C. Nevins ◽  
Kathryn Quoi ◽  
Richard K. Hailstone ◽  
Eric Lifshin
Keyword(s):  
Particle Size ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Point Spread Function ◽  
Background Correction ◽  
Support Material ◽  
Material Thickness ◽  
Point Spread ◽  
Spread Function ◽  
Scanning Electron

AbstractThe point spread function (PSF) of the scanning electron microscope (SEM) can be determined using a recently developed nanoparticle calibration method. Many parameters are involved in PSF determination and introduce a previously unstudied amount of uncertainty into the PSF size and shape. Signal type, support material thickness, reference particle size, PSF smoothing (K), and background correction were investigated regarding their effect on the PSF. Experimental data were complemented by CASINO simulations. Differences in detector position between the observed particles and the method's simulated reference particles caused shifting between secondary electron PSFs and backscattered electron PSFs. Support material thickness did not have a practical effect on the PSF at the tested voltages. Uncertainty in reference particle size varied the PSF full width at half maximum (FWHM) within ±0.7 nm at 2σ, with virtually no uncertainty in some cases. K and background correction within a reasonable range of values resulted in PSF FWHM differences within ±0.9 nm, except at 2 kV for K with an upper bound of ±1.9 nm due to increased noise. Tailoring K and background correction case-by-case would result in smaller differences. The interconnection of these parameters may help in future efforts to calculate their best selection.

scholarly journals Measurement of the Electron Beam Point Spread Function (PSF) in a Scanning Electron Microscope (SEM)

2015 ◽  
Vol 21 (S3) ◽  
pp. 699-700 ◽  
Author(s):  
Yudhishthir P. Kandel ◽  
Matthew D. Zotta ◽  
Andrew N. Caferra ◽  
Richard Moore ◽  
Eric Lifshin
Keyword(s):  
Electron Beam ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Point Spread Function ◽  
Point Spread ◽  
Spread Function ◽  
Scanning Electron

Wiener Filter Based on the Image to Blur

2012 ◽  
Vol 591-593 ◽  
pp. 1567-1570
Author(s):  
Chao Da Chen ◽  
Si Qing Zhang ◽  
Chui Xin Chen
Keyword(s):  
Image Quality ◽  
Image Restoration ◽  
Point Spread Function ◽  
Signal To Noise Ratio ◽  
Wiener Filter ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Ill Posed ◽  
Fuzzy Image

Image restoration, refers to the removal or loss in the process of getting digital image degradation of the image quality, image restoration technology is the key to meet the requirements of the point spread function, degradation model is an ill-posed integral equations, in the frequency domain, when H ( U, V ) less or equal to zero, the noise will be amplified, the degraded image and interference in H ( U, V ) value of the spectrum will be small to restore the image influence. In view of the point spread function put forward Wiener filtering algorithm, the noise lead to ill-posed integral with specified signal-to-noise ratio to reduce image restoration effects, through the IPT toolbox for fuzzy image restoration, image quality to achieve the anticipated effect.

Observability of Very Thick Specimen by Using Transmission Scanning Electron Microscope

1971 ◽  
Vol 29 ◽  
pp. 26-27
Author(s):  
K. Shibatomi ◽  
T. Yamanoto ◽  
H. Koike
Keyword(s):  
Electron Microscope ◽  
Image Quality ◽  
Scanning Electron Microscope ◽  
Chromatic Aberration ◽  
Image Contrast ◽  
Objective Lens ◽  
Thick Specimen ◽  
Transmission Electron ◽  
Scanning Electron

In the observation of a thick specimen by means of a transmission electron microscope, the intensity of electrons passing through the objective lens aperture is greatly reduced. So that the image is almost invisible. In addition to this fact, it have been reported that a chromatic aberration causes the deterioration of the image contrast rather than that of the resolution. The scanning electron microscope is, however, capable of electrically amplifying the signal of the decreasing intensity, and also free from a chromatic aberration so that the deterioration of the image contrast due to the aberration can be prevented. The electrical improvement of the image quality can be carried out by using the fascionating features of the SEM, that is, the amplification of a weak in-put signal forming the image and the descriminating action of the heigh level signal of the background. This paper reports some of the experimental results about the thickness dependence of the observability and quality of the image in the case of the transmission SEM.

Effect of Kaolin Particle Size Towards Preparation of Kaolin Ceramic Membrane

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Siti Khadijah Hubadillah ◽  
◽  
Norsiah Hami ◽  
Nurul Azita Salleh ◽  
Mohd Riduan Jamalludin ◽  
...  
Keyword(s):  
Particle Size ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Field Emission ◽  
Preliminary Data ◽  
Membrane Structure ◽  
Ceramic Membrane ◽  
Low Cost ◽  
Dense Structure ◽  
Scanning Electron

The purpose of this work is to study the effect of kaolin particle size for the preparation of low cost ceramic membrane suspension and ceramic membrane structure. Kaolin particle size is categorized into two categories; i) ≤ 1µm and ii) ≥ 1 µm. The suspension is prepared via stirring technique under 1000 rpm at 60°C. The particle size of kaolin is characterized using field emission scanning electron microscope (FESEM) and the prepared suspension is characterized in term of its viscosity. Results indicate that the particle size gave significant effect to the viscosity of ceramic membrane suspension. Preliminary data showed that kaolin with particle size ≤ 1µm resulted ceramic membrane with dense structure.

Optimization of PET/CT image quality using the GE ‘Sharp IR’ point-spread function reconstruction algorithm

2017 ◽  
Vol 38 (6) ◽  
pp. 471-479 ◽  
Author(s):  
Nicholas J. Vennart ◽  
Nicholas Bird ◽  
John Buscombe ◽  
Heok K. Cheow ◽  
Ewa Nowosinska ◽  
...  
Keyword(s):  
Image Quality ◽  
Point Spread Function ◽  
Reconstruction Algorithm ◽  
Ct Image ◽  
Point Spread ◽  
Pet Ct ◽  
Spread Function ◽  
Ct Image Quality ◽  
Function Reconstruction

Automation of size fractionation to extract clays and silts

Clay Minerals ◽  
2011 ◽  
Vol 46 (3) ◽  
pp. 515-523 ◽  
Author(s):  
Youjun Deng ◽  
M. G. Tenorio Arvide
Keyword(s):  
Particle Size ◽  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Laser Diffraction ◽  
Size Fractionation ◽  
Microscope Examination ◽  
Household Appliance ◽  
Lawn Irrigation ◽  
Sedimentation Time ◽  
Scanning Electron

AbstractThe objective of this study was to build an automated size fractionator to process up to 16 samples at one time. Most parts used in the apparatus are inexpensive items, available from lawn irrigation, household appliance and aquatic pet supply stores. The device can be used to extract different silt and clay fractions by changing sedimentation time. A bentonite, a kaolin and an ironoxide-rich Oxisol were fractionated by this instrument to sequentially extract particles that have sizes equivalent to <2 µm, <5 µm, <10 µm and <20 µm quartz spheres. A laser diffraction particle size analyser revealed size differences in the different fractions and also showed that the silt fractions contained particles having slightly larger sizes than the assumed diameters of spherical quartz. Scanning electron microscope examination suggested that the greater particle size was mainly due to the non-spherical shapes of the particles and a reduced bulk density of the porous aggregates.

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