Learning-Based Image Transport Through Disordered Optical Fibers With Transverse Anderson Localization

Fiber-optic imaging systems play a unique role in biomedical imaging and clinical practice due to their flexibilities of performing imaging deep into tissues and organs with minimized penetration damage. Their imaging performance is often limited by the waveguide mode properties of conventional optical fibers and the image reconstruction method, which restrains the enhancement of imaging quality, transport robustness, system size, and illumination compatibility. The emerging disordered Anderson localizing optical fibers circumvent these difficulties by their intriguing properties of the transverse Anderson localization of light, such as single-mode-like behavior, wavelength independence, and high mode density. To go beyond the performance limit of conventional system, there is a growing interest in integrating the disordered Anderson localizing optical fiber with deep learning algorithms. Novel imaging platforms based on this concept have been explored recently to make the best of Anderson localization fibers. Here, we review recent developments of Anderson localizing optical fibers and focus on the latest progress in deep-learning-based imaging applications using these fibers.

Automated summary

Fiber-optic imaging systems have unique advantages in imaging deep into tissues, but are limited by conventional optical fiber waveguide modes and image reconstruction methods. Emerging disordered Anderson localizing optical fibers overcome these limitations and, when integrated with deep learning algorithms, offer enhanced imaging capabilities.