Evolution of resolution in microscopy

Sarıkaya, Mehmet

doi:10.1016/0304-3991(92)90181-i

Cited by 13 publications

(4 citation statements)

References 48 publications

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“…In understanding these measurements, it is important to distinguish the “optical resolution” of a microscope from the ability to determine the center of an object. The optical resolution refers to the ability to distinguish two nearly touching objects. , Various measures exist (e.g., the Rayleigh limit, the Sparrow limit), but they are all similar: The optical resolution ( h ) is given by h = λ/2NA, where the wavelength of light (λ) is roughly 500 nm and the numerical aperture (NA) of our oil immersion objective is 1.30. In our case, h ≈ 200 nm.…”

Section: Resultsmentioning

confidence: 99%

“…Two advantages of EM are that the technique has a proven history of reliability and that it can visualize details of the particle surface. EM methods utilizing a short-wavelength electron beam provide much higher resolution than the optical microscope, even though they have some inherent characteristics that limit the practically attainable resolution (e.g., convolution of the aberration coefficient, 17 size of the scanning electron probe, and finite size of the effective interaction volume as in SEM 19 ). However, the accuracy is still dependent on the error involved in measuring the magnification.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Measuring Particle Diameter and Particle−Particle Gap with Nanometer Precision Using an Optical Microscope

Thwar¹,

Velegol²

2001

Ind. Eng. Chem. Res.

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A simple optical microscopy method has been developed for measuring particle diameters and particle-particle gaps with a precision of a few nanometers. The method uses an inexpensive and straightforward image-processing technique to examine optical microscope images of the particles, which can be wet or dry. The technique is demonstrated on polystyrene latex and silica particles with diameters greater than 450 nm suspended in KCl solutions, and the measured particle diameters compare to within a few percent with those measured by electron microscopy and light scattering. In addition, changes in the gap between two colloidal spheres were measured with nanometer precision, a result that will have important implications for measuring colloidal force profiles between two Brownian particles.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Measuring Particle Diameter and Particle−Particle Gap with Nanometer Precision Using an Optical Microscope

Thwar¹,

Velegol²

2001

Ind. Eng. Chem. Res.

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“…In optical microscopy, blur is typically caused by aberrations and diffraction [188]. More than 100 years of research, tracing back to Airy and Rayleigh's observations, have been oriented toward modifying the optical hardware-in our language, designing the illumination and collection operators-to compensate for the blur and obtain sharp images of objects down to sub-micrometer size.…”

Section: A Super-resolutionmentioning

confidence: 99%

On the use of deep learning for computational imaging

Barbastathis

Ozcan

Situ

2019

Optica

601

225

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Since their inception in the 1930-1960s, the research disciplines of computational imaging and machine learning have followed parallel tracks and, during the last two decades, experienced explosive growth drawing on similar progress in mathematical optimization and computing hardware. While these developments have always been to the benefit of image interpretation and machine vision, only recently has it become evident that machine learning architectures, and deep neural networks in particular, can be effective for computational image formation, aside from interpretation. The deep learning approach has proven to be especially attractive when the measurement is noisy and the measurement operator ill posed or uncertain. Examples reviewed here are: super-resolution; lensless retrieval of phase and complex amplitude from intensity; photon-limited scenes, including ghost imaging; and imaging through scatter. In this paper, we cast these works in a common framework. We relate the deep-learning-inspired solutions to the original computational imaging formulation and use the relationship to derive design insights, principles, and caveats of more general applicability. We also explore how the machine learning process is aided by the physics of imaging when ill posedness and uncertainties become particularly severe. It is hoped that the present unifying exposition will stimulate further progress in this promising field of research.

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“…Despite the great benefit such high resolution, modern light microscopes have brought into life sciences, the nowadays commercially available instruments show principle short-comings especially for nano-structure investigations. Diffraction and other non-ideal optical conditions of biological specimens like variations of the refraction index limit the spatial resolution of a far-field light microscope [51,53] in practice to about 250 nm or even worse [15,28]. A general approach to overcome this limitation in fluorescence far-field microscopy is spectral precision distance microscopy [10], a technique that has been successfully applied in cytogenetics (e.g., [17]).…”

Section: Introductionmentioning

confidence: 99%

Variations in Cell Surfaces of Estrogen Treated Breast Cancer Cells Detected by A Combined Instrument for Far‐Field and Near‐Field Microscopy

Perner¹,

Rapp²,

Dressler

et al. 2002

Analytical Cellular Pathology

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The response of single breast cancer cells (cell line T‐47D) to 17β‐estradiol (E2) under different concentrations was studied by using an instrument that allows to combine far‐field light microscopy with high resolution scanning near‐field (AFM/SNOM) microscopy on the same cell. Different concentrations of E2 induce clearly different effects as well on cellular shape (in classical bright‐field imaging) as on surface topography (atomic force imaging) and absorbance (near‐field light transmission imaging). The differences range from a polygonal shape at zero via a roughly spherical shape at physiological up to a spindle‐like shape at un‐physiologically high concentrations. The surface topography of untreated control cells was found to be regular and smooth with small overall height modulations. At physiological E2 concentrations the surfaces became increasingly jagged as detected by an increase in membrane height. After application of the un‐physiological high E2 concentration the cell surface structures appeared to be smoother again with an irregular fine structure. The general behaviour of dose dependent differences was also found in the near‐field light transmission images. In order to quantify the treatment effects, line scans through the normalised topography images were drawn and a rate of co‐localisation between high topography and high transmission areas was calculated. The cell biological aspects of these observations are, so far, not studied in detail but measurements on single cells offer new perspectives to be empirically used in diagnosis and therapy control of breast cancers.

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Evolution of resolution in microscopy

Cited by 13 publications

References 48 publications

Measuring Particle Diameter and Particle−Particle Gap with Nanometer Precision Using an Optical Microscope

Measuring Particle Diameter and Particle−Particle Gap with Nanometer Precision Using an Optical Microscope

On the use of deep learning for computational imaging

Variations in Cell Surfaces of Estrogen Treated Breast Cancer Cells Detected by A Combined Instrument for Far‐Field and Near‐Field Microscopy

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