{"title":"Optical Computational Imaging Through Unknown Random Diffusers in Visible Spectrum","authors":"Lin Huang, Yongyin Cao, Shiyong Ren, Qi Jia, Bojian Shi, Rui Feng, Fangkui Sun, Jian Wang, Yongkang Dong, Weiqiang Ding","doi":"10.1002/lpor.202501168","DOIUrl":null,"url":null,"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.","PeriodicalId":204,"journal":{"name":"Laser & Photonics Reviews","volume":"100 1","pages":""},"PeriodicalIF":10.0000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Laser & Photonics Reviews","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1002/lpor.202501168","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
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.
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.