Add to ORKGWe demonstrate for the first time that deep neural networks (DNNs) can be trained to recover images transported through a 90 cm-long silica-air disordered optical fiber at variable working distances without any distal optics
Recent burgeoning machine learning has revolutionized our ways of looking at the world. Being extrao...
As the representative of flexibility in optical imaging media, in recent years, fiber bundles have e...
Exponential advancements in computational resources and algorithms have given birth to the new parad...
We demonstrate for the first time that deep neural networks (DNNs) can be trained to recover images ...
We demonstrate a fully flexible, artifact-free, and lensless fiber-based imaging system. For the fir...
We demonstrate that images can be reconstructed for objects away from the imaging plane without any ...
We demonstrate a bending-independent imaging system for the first time by combining deep neural netw...
The fiber-optic imaging system enables imaging deeply into hollow tissue tracts or organs of biologi...
Robust optical image transport through 90cm-long low-loss silica-air disordered fiber isreported for...
Optical image transport through low-loss silica-air based disordered fiber is reported for the first...
Image delivery through multimode fibers (MMFs) suffers from modal scrambling which results in a spec...
Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent li...
We propose a deep learning based method to estimate high-resolution images from multiple fiber bundl...
Image transmission through a multi-mode fiber is a difficult task given the complex interference of ...
Deep learning has been proven to yield reliably generalizable solutions to numerous classification a...
Recent burgeoning machine learning has revolutionized our ways of looking at the world. Being extrao...
As the representative of flexibility in optical imaging media, in recent years, fiber bundles have e...
Exponential advancements in computational resources and algorithms have given birth to the new parad...
We demonstrate for the first time that deep neural networks (DNNs) can be trained to recover images ...
We demonstrate a fully flexible, artifact-free, and lensless fiber-based imaging system. For the fir...
We demonstrate that images can be reconstructed for objects away from the imaging plane without any ...
We demonstrate a bending-independent imaging system for the first time by combining deep neural netw...
The fiber-optic imaging system enables imaging deeply into hollow tissue tracts or organs of biologi...
Robust optical image transport through 90cm-long low-loss silica-air disordered fiber isreported for...
Optical image transport through low-loss silica-air based disordered fiber is reported for the first...
Image delivery through multimode fibers (MMFs) suffers from modal scrambling which results in a spec...
Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent li...
We propose a deep learning based method to estimate high-resolution images from multiple fiber bundl...
Image transmission through a multi-mode fiber is a difficult task given the complex interference of ...
Deep learning has been proven to yield reliably generalizable solutions to numerous classification a...
Recent burgeoning machine learning has revolutionized our ways of looking at the world. Being extrao...
As the representative of flexibility in optical imaging media, in recent years, fiber bundles have e...
Exponential advancements in computational resources and algorithms have given birth to the new parad...