Add to ORKGWe demonstrate a bending-independent imaging system for the first time by combining deep neural networks (DNNs) and a meter-long silica-air disordered optical fiber. High-quality artifact-free images can be reconstructed from the transported raw images
We present the first attempt to perform short glass fiber segmentation from x-ray computed tomograph...
We present a randomly disordered silica-air optical fiber featuring a 28.5% air filling fraction in ...
As the representative of flexibility in optical imaging media, in recent years, fiber bundles have e...
We demonstrate that images can be reconstructed for objects away from the imaging plane without any ...
We demonstrate a fully flexible, artifact-free, and lensless fiber-based imaging system. For the fir...
We demonstrate for the first time that deep neural networks (DNNs) can be trained to recover images ...
The fiber-optic imaging system enables imaging deeply into hollow tissue tracts or organs of biologi...
We propose a deep learning based method to estimate high-resolution images from multiple fiber bundl...
Deep learning has been proven to yield reliably generalizable solutions to numerous classification a...
In this paper, a learning-based fiber specklegram sensor for bending recognition is proposed and dem...
As we all know, the change of mode interference caused by the curvature change in multi-mode fiber (...
Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent li...
We propose a data -driven approach for light transmission control inside multimode fibers (MMFs). Sp...
We propose a deep learning-based restoration method to remove honeycomb patterns and improve resolut...
A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. However, s...
We present the first attempt to perform short glass fiber segmentation from x-ray computed tomograph...
We present a randomly disordered silica-air optical fiber featuring a 28.5% air filling fraction in ...
As the representative of flexibility in optical imaging media, in recent years, fiber bundles have e...
We demonstrate that images can be reconstructed for objects away from the imaging plane without any ...
We demonstrate a fully flexible, artifact-free, and lensless fiber-based imaging system. For the fir...
We demonstrate for the first time that deep neural networks (DNNs) can be trained to recover images ...
The fiber-optic imaging system enables imaging deeply into hollow tissue tracts or organs of biologi...
We propose a deep learning based method to estimate high-resolution images from multiple fiber bundl...
Deep learning has been proven to yield reliably generalizable solutions to numerous classification a...
In this paper, a learning-based fiber specklegram sensor for bending recognition is proposed and dem...
As we all know, the change of mode interference caused by the curvature change in multi-mode fiber (...
Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent li...
We propose a data -driven approach for light transmission control inside multimode fibers (MMFs). Sp...
We propose a deep learning-based restoration method to remove honeycomb patterns and improve resolut...
A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. However, s...
We present the first attempt to perform short glass fiber segmentation from x-ray computed tomograph...
We present a randomly disordered silica-air optical fiber featuring a 28.5% air filling fraction in ...
As the representative of flexibility in optical imaging media, in recent years, fiber bundles have e...