Multimode fiber (MMF) plays a vital role in promoting the miniaturization of endoscope. However, real-time and high-definition imaging using the MMF that remains a challenging research.
When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber
{abstract*} Multimode fiber (MMF) imaging aided by machine learning holds promise for numerous applications, including medical endoscopy. A key challenge for this technology is the sensitivity of
Semantic Scholar extracted view of "Image transmission through a multimode fiber based on transfer learning" by Yong Zhang et al.
Image transmission through multimode fiber (MMF) has great potential to innovate the systems of endoscopic or neurological imaging. However, when injecting a li
We propose a physics-informed deep learning framework for efficient wavelength-multiplexed image transmission through multimode fiber. Experimental results demonstrate its potential for preserving
In this paper, we combine principal component analysis (PCA) method, deep learning based speckle classification (DLSC) and deep learning based image enhancement (DLIE) to improve
This presents a major difficulty for many possible applications, such as multimode fiber-based telecommunication and endoscopy. To overcome this challenge, a deep learning approach
Abstract High-quality signal transmission and imaging through a multimode fiber is essential for optical communications and medical endoscopic
Multimode fibers (MMFs) enable high-resolution imaging due to their capacity to support numerous spatial modes within a compact and minimally invasive form factor. However, inherent
Multimode fibers (MMF) are an example of a highly scattering medium which scramble the coherent light propagating within them and produce seemingly random patterns. Thus, for applications such as
Due to the applications in the fields of optical communication, neuronal imaging, and medical endoscopic imaging, the study of multimode fiber (MMF) wavefront transmission is crucial for
We build a single-arm multimode fiber image transmission system. The impact of five different sources on transmission quality is systematically
Experimental results demonstrate its potential for preserving high-fidelity information transfer while ensuring robustness and high resolution in multimode fiber systems.
There is an upward trend in using multimode fiber for an increasing number of applications such as optical telecommunication, endoscopic imaging or laser beam shaping, which require knowledge of
Here we develop a miniaturized diffractive neural network (DN2s) integrated on the distal facet of a MMF for the direct all-optical image
Using this approach, successful reconstruction of the input images from intensity-only measurements of speckle patterns at the output of a 1.5 m
High-fidelity image transmission through multimode fiber is critical for the biomedical imaging and telecommunications industries. However, mode coupling and modal dispersion usually
Here we implement a method that statistically reconstructs the inverse transformation matrix for the fibre. We demonstrate imaging at high frame rates, high resolutions and in full colour of natural scenes,
The multimode fiber is a kind of scattering medium, in which the light travels along different optical modes with different phase speeds. High-quality optical communications and medical
Image projection through a multimode fiber (MMF) or scattering media has applications ranging from optogenetics to near eye-displays. It requires developing computer-generated
Multimode fibers provide a unique opportunity for exploring the spatial degrees of freedom for high throughput light transmission. However, the
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