• Current Optics and Photonics
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• 제6권6호
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• pp.550-564
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• 2022

Optical microscopy is a useful tool for study in the biological sciences. With an optical microscope, we can observe the micro world of life such as tissues, cells, and proteins. A fluorescent dye or a fluorescent protein provides an opportunity to mark a specific target in the crowd of biological samples, so that an image of a specific target can be observed by an optical microscope. The optical microscope, however, is constrained in resolution due to diffraction limit. Super-resolution microscopy made a breakthrough with this diffraction limit. Using a super-resolution microscope, many biomolecules are observed beyond the diffraction limit in cells. In the case of volumetric imaging, the super-resolution techniques are only applied to a limited area due to long imaging time, multiple scattering of photons, and sample-induced aberration in deep tissue. In this article, we review recent advances in super-resolution microscopy for volumetric imaging. The super-resolution techniques have been integrated with various modalities, such as a line-scan confocal microscope, a spinning disk confocal microscope, a light sheet microscope, and point spread function engineering. Super-resolution microscopy combined with adaptive optics by compensating for wave distortions is a promising method for deep tissue imaging and biomedical applications.

• 한국재료학회지
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• 제32권8호
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• pp.345-353
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• 2022

In this study, using deep learning, super-resolution images of transmission electron microscope (TEM) images were generated for nanomaterial analysis. 1169 paired images with 256 × 256 pixels (high resolution: HR) from TEM measurements and 32 × 32 pixels (low resolution: LR) produced using the python module openCV were trained with deep learning models. The TEM images were related to DyVO₄ nanomaterials synthesized by hydrothermal methods. Mean-absolute-error (MAE), peak-signal-to-noise-ratio (PSNR), and structural similarity (SSIM) were used as metrics to evaluate the performance of the models. First, a super-resolution image (SR) was obtained using the traditional interpolation method used in computer vision. In the SR image at low magnification, the shape of the nanomaterial improved. However, the SR images at medium and high magnification failed to show the characteristics of the lattice of the nanomaterials. Second, to obtain a SR image, the deep learning model includes a residual network which reduces the loss of spatial information in the convolutional process of obtaining a feature map. In the process of optimizing the deep learning model, it was confirmed that the performance of the model improved as the number of data increased. In addition, by optimizing the deep learning model using the loss function, including MAE and SSIM at the same time, improved results of the nanomaterial lattice in SR images were achieved at medium and high magnifications. The final proposed deep learning model used four residual blocks to obtain the characteristic map of the low-resolution image, and the super-resolution image was completed using Upsampling2D and the residual block three times.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제53권1호
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• pp.30-35
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• 2004

We have carried out a basic experiment in order to develope a super high-resolution optical microscope which transcend the limitation of diffraction and the wavelength of lightwave. The image of this scope is composed by measuring the evanescent wave which is localized on the surface of the testing materials. A detecting probe was fabricated with a single mode optical fiber to be sharpened by the chemical etching, and drived by PZT. The standing wave of $0.33\mu\textrm{m}$ wavelength evanescent wave which was generated from the $0.78\mu\textrm{m}$-wavelength semiconductor laser was detected by the $0.5\mu\textrm{m}$-thickness optical fiber probe.