Single multimode fiber imaging based on low-rank recovery. (February 2022) - Yang, Dongyue; Hao, Min; Wu, Guohua; Chang, Chen; Luo, Bin; Yin, Longfei
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.