Low-power scanning imaging based on multimode fiber and non-local mean filtering

• Published in: Optics and lasers in engineering Vol. 185; p. 108715
• Main Authors: Zhang, Yiyang, Ding, Zhewen, Shi, Yan, Chen, Jun, Zhan, Chunlian, Zhao, Chunliu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2025
• Subjects: Digital holography; Fiber optics imaging; Image processing; Low-power imaging; Transmission matrix; Fiber optics imaging; Digital holography; Low-power imaging; Image processing; Transmission matrix
• ISSN: 0143-8166
• DOI: 10.1016/j.optlaseng.2024.108715

Due to the ability to flexibly transmit optical signals and high spatial resolution, multimode fibers (MMFs) show great potential in micrometer-scale imaging, which can be used to observe biological organisms and living cells. To minimize damage to the observed subject, milliwatt to microwatt level illumination should be used for imaging, but lower optical power results in a lower signal-to-noise ratio of the imaged signal, and consequently the poorer imaging quality. In this paper, the effects of environmental and device noise on the imaging performance at low-power input are discussed through the simulation, and a low-power scanning imaging method based on the MMF and non-local mean (NLM) filtering is proposed for the first time. To validate the effectiveness of the proposed method, a low-power scanning imaging system based on MMF was built, and the effects of Gaussian filtering and NLM filtering on imaging results were compared. Experimental results show that NLM filtering is five times more effective than Gaussian filtering in improving the imaging resolution by about 12%. This work holds promise for providing new approaches and methods for low-power endoscopic imaging and in vivo cell imaging in biological organisms. •The holographic wavefront shaping technique is used to calibrate the distal light of the fiber.•Scanning imaging is performed under low-power conditions at the microwatt level.•Optimal selection of parameters in the non-local mean filtering algorithm by genetic algorithm.•The NLM algorithm enhances the system's spatial resolution by 12%, outperforming Gaussian filtering by a factor of 4.