Toward Micro-Endoscopy: Distal-Free, Configuration-Agnostic Focusing Through Multimode Fiber

Abstract

Multimode fibers (MMFs) can transmit multiple guided modes simultaneously, making them a promising platform for high-resolution biomedical imaging, endoscopy and high-bandwidth optical communication. However, their complex modal behavior, influenced by environmental perturbations and mode coupling, presents a major challenge for accurate wavefront control. Conventional approaches for shaping the light at their output typically rely on the transmitted field as a source for iterative feedback, making it impractical for in-situ applications where direct access to the transmission is impossible. Here, we introduce a deep learning-based framework for predicting transmission through MMF by observing only the reflected signal. Harnessing the reflected signals that encode the fiber's internal configuration, our approach not only generalizes across varying fiber conditions but also enables focusing through the fiber without requiring transmission feedback. By training the system experimentally using a dataset of 4 million images across 1200 distinct fiber configurations, we demonstrate robust and precise wavefront reconstruction even under significant perturbations. Our results underscore the potential of learning-based techniques for real-time MMF-based imaging and optical communications, paving the way for efficient non-invasive focusing in practical applications.