Optical Computational Imaging Through Unknown Random Diffusers in Visible Spectrum

IF 10 1区 物理与天体物理 Q1 OPTICS
Lin Huang, Yongyin Cao, Shiyong Ren, Qi Jia, Bojian Shi, Rui Feng, Fangkui Sun, Jian Wang, Yongkang Dong, Weiqiang Ding
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引用次数: 0

Abstract

Imaging through random scattering media is an important challenge in computational optics. Diffractive Optical Neural Networks (DONNs) have recently been demonstrated to efficiently recover images with scattering distortions using coherent light in terahertz spectrum [eLight, 2022, 2(1):4]. However, the study [Optica, 2024, 11(12):1742] has demonstrated that DONN designed for coherent light may not function optimally with low coherent sources. Consequently, the performance of DONNs in image‐based reconstruction tasks under visible and incoherent light requires further investigation. For this purpose, a three‐layer DONN is constructed to reconstruct images occluded by unknown random diffusers under coherent/incoherent visible light. The results show that the Pearson correlation coefficient (PCC) of the coherent DONNs reconstructed images can reach 0.863–0.971 for diffuser correlation lengths of (: a single neuron size of 8 µm); when of , the PCC of the incoherent diffractive optical neural networks (IC‐DONNs) reconstructed images can reach 0.861–0.899. It is found that the dynamic phase modulation mechanism introduced by randomly generated diffusers during network training enhances the adaptation of coherent DONN to spatial coherence variations of the light source. It is believed that these findings will advance the application of DONN for imaging under natural environmental conditions.
可见光光谱中未知随机漫射的光学计算成像
随机散射介质成像是计算光学中的一个重要课题。衍射光学神经网络(DONNs)最近被证明可以有效地利用太赫兹光谱中的相干光恢复具有散射畸变的图像[eLight, 2022, 2(1):4]。然而,这项研究[光学,2024,11(12):1742]表明,为相干光设计的DONN在低相干光源下可能无法发挥最佳作用。因此,在可见光和非相干光下,donn在基于图像的重建任务中的表现需要进一步研究。为此,在相干/非相干可见光下,构建了一个三层DONN来重建被未知随机漫射遮挡的图像。结果表明:当扩散器相关长度为(单个神经元大小为8µm)时,相干DONNs重构图像的Pearson相关系数(PCC)可达0.863 ~ 0.971;的时候,非相干衍射光神经网络(IC - DONNs)重构图像的PCC可以达到0.861-0.899。研究发现,随机产生的扩散粒子在网络训练过程中引入的动态相位调制机制增强了相干DONN对光源空间相干变化的适应能力。相信这些发现将推动DONN在自然环境条件下成像的应用。
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来源期刊
CiteScore
14.20
自引率
5.50%
发文量
314
审稿时长
2 months
期刊介绍: Laser & Photonics Reviews is a reputable journal that publishes high-quality Reviews, original Research Articles, and Perspectives in the field of photonics and optics. It covers both theoretical and experimental aspects, including recent groundbreaking research, specific advancements, and innovative applications. As evidence of its impact and recognition, Laser & Photonics Reviews boasts a remarkable 2022 Impact Factor of 11.0, according to the Journal Citation Reports from Clarivate Analytics (2023). Moreover, it holds impressive rankings in the InCites Journal Citation Reports: in 2021, it was ranked 6th out of 101 in the field of Optics, 15th out of 161 in Applied Physics, and 12th out of 69 in Condensed Matter Physics. The journal uses the ISSN numbers 1863-8880 for print and 1863-8899 for online publications.
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