Single multimode fiber imaging based on low-rank recovery. (February 2022)

• Record Type: Journal Article
• Authors: Yang, Dongyue; Hao, Min; Wu, Guohua; Chang, Chen; Luo, Bin; Yin, Longfei
• Is Part Of: Optics and lasers in engineering. Volume 149(2022)
• Publication Date: 2022-02
• Subjects: Multimode fiber imaging -- Compressive sensing -- Low-rank recovery; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2021.106827
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: A novel scanning-free multimode fiber imaging scheme exploiting the low-rank properties of objects. The low-rank recovery methods takes the advantage of superior reconstruction quality and higher sampling efficiency. Robustness of the multimode fiber imaging using low-rank recovery against fiber bending is also investigated. Abstract: We present a novel scheme of single multimode fiber imaging by exploiting low-rank constraint for a faithful image recovery. Compared with the commonly-adopted sparsity constraint, the proposed scheme takes the advantage of the self-similarity property of natural images, and is demonstrated to achieve higher image quality and smoothness in multimode fiber imaging, especially in the under-sampling cases. We also demonstrate the robustness of this method to recover different kind of images against fiber bending, and discuss the low-rank parameter tunning for a stable recovery. Our findings may pave the way for a simpler and more robust scheme of single multimode fiber imaging, which could be applied for many demanding imaging tasks.