A novel amino-modifier ESF nucleoside AM37zA (1) containing trifluoroacetyl (TFA)-protected amino group is designed for the functionalization of ODN probe after oligonucleotide synthesis. AM37zA (1) demonstrated remarkable solvatochromicity and ODN...
Multispectral imaging through scattering media is an important practical issue in the
field of sensing. The light from a scattering medium is expected to
carry information about the spectral properties of the medium, as well
as geometrical information. Because spatial and spectral information
of the object is encoded in speckle images, the information about the
structure and spectrum of the object behind the scattering medium can
be estimated from those images. Here we propose a deep learning-based
strategy that can estimate the central wavelength from speckle images
captured with a monochrome camera. When objects behind scattering
media are illuminated with narrowband light having different spectra
with different spectral peaks, deep learning of speckle images
acquired at different central wavelengths can extend the spectral
region to reconstruct images and estimate the central wavelengths of
the illumination light. The proposed method achieves central
wavelength estimation in 1 nm steps for objects whose central
wavelength varies in a range of 100 nm. Because our method can
achieve image reconstruction and central wavelength estimation in a
single shot using a monochrome camera, this technique will pave the
way for multispectral imaging through scattering media.
.
Significance:
The imaging of objects hidden in light-scattering media is a vital practical task in a wide range of applications, including biological imaging. Deep-learning-based methods have been used to reconstruct images behind scattering media under complex scattering conditions, but improvements in the quality of the reconstructed images are required.
Aim:
To investigate the effect of image plane on the accuracy of reconstructed images.
Approach:
Light reflected from an object passing through glass diffusers is captured by changing the image plane of an optical imaging system. Images are reconstructed by deep learning, and evaluated in terms of structural similarity index measure, classification accuracy of digital images, and training and testing error curves.
Results:
The reconstruction accuracy was improved for the case in which the diffuser was imaged, compared to the case where the object was imaged. The training and testing error curves show that the loss converged to lower values in fewer epochs when the diffuser was imaged.
Conclusions:
The proposed approach demonstrates an improvement in the accuracy of the reconstruction of objects hidden through glass diffusers by imaging glass diffuser surfaces, and can be applied to objects at unknown locations in a scattering medium.
When light propagates through a scattering medium, imaging of an object hidden behind the scattering medium is difficult due to wavefront distortion. Scattering imaging is a technique for reconstructing images by solving the problem of complex reconstruction from speckle images. Tracking moving targets behind a scattering medium is a challenge. Scattering imaging using deep learning is a robust technique that learns a huge number of pairs of ground-truth images and speckle images. Here, we demonstrate tracking of moving targets with an extended-depth of field (DOF) behind a scattering medium based on deep learning of speckle images acquired at different depths. We found that it was possible to track moving targets over a wide axial direction by increasing the number of trained positions.
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