{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T15:27:04Z","timestamp":1776698824656,"version":"3.51.2"},"reference-count":80,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002613","name":"U-K Brand Research Fund","doi-asserted-by":"publisher","award":["1.220027.01"],"award-info":[{"award-number":["1.220027.01"]}],"id":[{"id":"10.13039\/501100002613","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002613","name":"U-K Brand Research Fund","doi-asserted-by":"publisher","award":["1711138075, KMDF_PR_20200901_0066"],"award-info":[{"award-number":["1711138075, KMDF_PR_20200901_0066"]}],"id":[{"id":"10.13039\/501100002613","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002613","name":"U-K Brand Research Fund","doi-asserted-by":"publisher","award":["2020R1A2B5B02001987"],"award-info":[{"award-number":["2020R1A2B5B02001987"]}],"id":[{"id":"10.13039\/501100002613","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002613","name":"U-K Brand Research Fund","doi-asserted-by":"publisher","award":["NRF-2020R1F1A1A01049528"],"award-info":[{"award-number":["NRF-2020R1F1A1A01049528"]}],"id":[{"id":"10.13039\/501100002613","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR.<\/jats:p>","DOI":"10.3390\/s22103961","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:14:14Z","timestamp":1653437654000},"page":"3961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing"],"prefix":"10.3390","volume":"22","author":[{"given":"Oleksandra","family":"Gulenko","sequence":"first","affiliation":[{"name":"Center for Photoacoustic Medical Instruments, Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyunmo","family":"Yang","sequence":"additional","affiliation":[{"name":"Translational Biophotonics Lab, Department of Biomedical Engineering, UNIST, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"KiSik","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Photoacoustic Medical Instruments, Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Young","family":"Youm","sequence":"additional","affiliation":[{"name":"Center for Photoacoustic Medical Instruments, Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjae","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Photoacoustic Medical Instruments, Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2679-0090","authenticated-orcid":false,"given":"Yunho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, UNIST, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Woonggyu","family":"Jung","sequence":"additional","affiliation":[{"name":"Translational Biophotonics Lab, Department of Biomedical Engineering, UNIST, Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joon-Mo","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Photoacoustic Medical Instruments, Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/TEMC.1977.303527","article-title":"Statistical-Physical Models of Electromagnetic Interference","volume":"EMC-19","author":"Middleton","year":"1977","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TEMC.2004.837671","article-title":"Electromagnetic Interference (EMI) Reduction from Printed Circuit Boards (PCB) Using Electromagnetic Bandgap Structures","volume":"46","author":"Shahparnia","year":"2004","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/TEMC.2010.2046419","article-title":"Systematic Electromagnetic Interference Filter Design Based on Information from In-Circuit Impedance Measurements","volume":"52","author":"Tarateeraseth","year":"2010","journal-title":"IEEE Trans. Electromagn. Compat."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1109\/TIA.2010.2058836","article-title":"Generalized Terminal Modeling of Electromagnetic Interference","volume":"46","author":"Baisden","year":"2010","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kaur, M., Kakar, S., and Mandal, D. (2011, January 8\u201310). Electromagnetic Interference. Proceedings of the 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, India.","DOI":"10.1109\/ICECTECH.2011.5941844"},{"key":"ref_6","unstructured":"Murakawa, K., Hirasawa, N., Ito, H., and Ogura, Y. (2014, January 12\u201316). Electromagnetic Interference Examples of Telecommunications System in the Frequency Range From 2kHz to 150kHz. Proceedings of the 2014 International Symposium on Electromagnetic Compatibility, Tokyo, Japan."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compositesa.2018.08.006","article-title":"Recent Advances in Electromagnetic Interference Shielding Properties of Metal and Carbon Filler Reinforced Flexible Polymer Composites: A Review","volume":"114","author":"Sankaran","year":"2018","journal-title":"Compos. A Appl. Sci. Manuf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1098\/rsfs.2011.0028","article-title":"Biomedical Photoacoustic Imaging","volume":"1","author":"Beard","year":"2011","journal-title":"Interface Focus"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1126\/science.1216210","article-title":"Photoacoustic Tomography: In Vivo Imaging from Organelles to Organs","volume":"335","author":"Wang","year":"2012","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.pacs.2014.04.002","article-title":"Sensitivity of Photoacoustic Microscopy","volume":"2","author":"Yao","year":"2014","journal-title":"Photoacoustics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/nmeth.3929","article-title":"Contrast Agents for Molecular Photoacoustic Imaging","volume":"13","author":"Weber","year":"2016","journal-title":"Nat. Methods"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1039\/C6CS00765A","article-title":"Advanced Optoacoustic Methods for Multiscale Imaging of In Vivo Dynamics","volume":"46","author":"Gottschalk","year":"2017","journal-title":"Chem. Soc. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhong, H., Duan, T., Lan, H., Zhou, M., and Gao, F. (2018). Review of Low-Cost Photoacoustic Sensing and Imaging Based on Laser Diode and Light-Emitting Diode. Sensors, 18.","DOI":"10.3390\/s18072264"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100141","DOI":"10.1016\/j.pacs.2019.100141","article-title":"Review on Practical Photoacoustic Microscopy","volume":"15","author":"Jeon","year":"2019","journal-title":"Photoacoustics"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/s41551-019-0377-4","article-title":"Optoacoustic Mesoscopy for Biomedicine","volume":"3","author":"Omar","year":"2019","journal-title":"Nat. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/978-981-33-6064-8_11","article-title":"Photoacoustic Tomography Opening New Paradigms in Biomedical Imaging","volume":"1310","author":"Yang","year":"2021","journal-title":"Adv. Exp. Med. Biol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, M., Awasthi, N., Rad, N.M., Pluim, J.P.W., and Lopata, R.G.P. (2021). Advanced Ultrasound and Photoacoustic Imaging in Cardiology. Sensors, 21.","DOI":"10.3390\/s21237947"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1117\/12.280297","article-title":"Laser Optoacoustic Tomography of Layered Tissue: Signal Processing","volume":"2979","author":"Oraevsky","year":"1997","journal-title":"Proc. SPIE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1364\/OL.34.001591","article-title":"Photoacoustic Endoscopy","volume":"34","author":"Yang","year":"2009","journal-title":"Opt. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1038\/nm.2823","article-title":"Simultaneous Functional Photoacoustic and Ultrasonic Endoscopy of Internal Organs In Vivo","volume":"18","author":"Yang","year":"2012","journal-title":"Nat. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1364\/BOE.6.000918","article-title":"Optical-Resolution Photoacoustic Endomicroscopy In Vivo","volume":"6","author":"Yang","year":"2015","journal-title":"Biomed. Opt. Express"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e201800034","DOI":"10.1002\/jbio.201800034","article-title":"In Vivo Photoacoustic\/Ultrasonic Dual-Modality Endoscopy with a Miniaturized Full Field-of-View Catheter","volume":"11","author":"Li","year":"2018","journal-title":"J. Biophotonics"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7102005","DOI":"10.1109\/JSTQE.2018.2869614","article-title":"High-Speed Integrated Endoscopic Photoacoustic and Ultrasound Imaging System","volume":"25","author":"Li","year":"2019","journal-title":"IEEE J. Sel. Top. Quantum. Electron."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bai, X., Gong, X., Hau, W., Lin, R., Zheng, J., Liu, C., Zeng, C., Zou, X., Zheng, H., and Song, L. (2014). Intravascular Optical-Resolution Photoacoustic Tomography with a 1.1 mm Diameter Catheter. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0092463"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1364\/BOE.8.000943","article-title":"Real-Time Volumetric Lipid Imaging In Vivo by Intravascular Photoacoustics at 20 Frames per Second","volume":"8","author":"Wu","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2400","DOI":"10.1038\/s41598-018-20881-5","article-title":"Fast Assessment of Lipid Content in Arteries In Vivo by Intravascular Photoacoustic Tomography","volume":"8","author":"Cao","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100262","DOI":"10.1016\/j.pacs.2021.100262","article-title":"IVUS\\IVPA Hybrid Intravascular Molecular Imaging of Angiogenesis in Atherosclerotic Plaques via RGDfk Peptide-Targeted Nanoprobes","volume":"22","author":"Lin","year":"2021","journal-title":"Photoacoustics"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1364\/BOE.420724","article-title":"Multi-Spectral Intravascular Photoacoustic\/Ultrasound\/Optical Coherence Tomography Tri-Modality System with a Fully-Integrated 0.9-mm Full Field-of-View Catheter for Plaque Vulnerability Imaging","volume":"12","author":"Leng","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100346","DOI":"10.1016\/j.pacs.2022.100346","article-title":"Intra-Instrument Channel Workable, Optical-Resolution Photoacoustic and Ultrasonic Mini-Probe System for Gastrointestinal Endoscopy","volume":"26","author":"Kim","year":"2022","journal-title":"Photoacoustics"},{"key":"ref_30","first-page":"115532W","article-title":"Application of Convolutional Neural Network in Signal Classification for In Vivo Photoacoustic Flow Cytometry","volume":"11553","author":"Song","year":"2020","journal-title":"Proc. SPIE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5457","DOI":"10.1109\/ACCESS.2018.2888910","article-title":"Photoacoustic Image Classification and Segmentation of Breast Cancer: A Feasibility Study","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shan, H., Wang, G., and Yang, Y. (2019). Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography. Appl. Sci., 9.","DOI":"10.3390\/app9132615"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100190","DOI":"10.1016\/j.pacs.2020.100190","article-title":"Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data","volume":"19","author":"Tong","year":"2020","journal-title":"Photoacoustics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1109\/TMI.2020.3031541","article-title":"Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning","volume":"40","author":"DiSpirito","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/JBHI.2019.2912935","article-title":"Fully Dense UNet for 2-D Sparse Photoacoustic Tomography Artifact Removal","volume":"24","author":"Guan","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100218","DOI":"10.1016\/j.pacs.2020.100218","article-title":"Compensating for Visibility Artefacts in Photoacoustic Imaging with a Deep Learning Approach Providing Prediction Uncertainties","volume":"21","author":"Godefroy","year":"2021","journal-title":"Photoacoustics"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s42492-019-0022-9","article-title":"Deep-Learning-Based Motion-Correction Algorithm in Optical Resolution Photoacoustic Microscopy","volume":"2","author":"Chen","year":"2019","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100197","DOI":"10.1016\/j.pacs.2020.100197","article-title":"Y-Net: Hybrid Deep Learning Image Reconstruction for Photoacoustic Tomography In Vivo","volume":"20","author":"Lan","year":"2020","journal-title":"Photoacoustics"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/s42256-019-0095-3","article-title":"Deep Learning Optoacoustic Tomography with Sparse Data","volume":"1","author":"Davoudi","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100310","DOI":"10.1016\/j.pacs.2021.100310","article-title":"Full-View In Vivo Skin and Blood Vessels Profile Segmentation in Photoacoustic Imaging Based on Deep Learning","volume":"25","author":"Ly","year":"2022","journal-title":"Photoacoustics"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"100203","DOI":"10.1016\/j.pacs.2020.100203","article-title":"A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography","volume":"20","author":"Chlis","year":"2020","journal-title":"Photoacoustics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1109\/TUFFC.2020.3022324","article-title":"Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images","volume":"68","author":"Lafci","year":"2021","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1364\/BOE.395683","article-title":"Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging","volume":"11","author":"Hariri","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2660","DOI":"10.1109\/TUFFC.2020.2977210","article-title":"Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography","volume":"67","author":"Awasthi","year":"2020","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1109\/TIP.2005.852196","article-title":"Salt-And-Pepper Noise Removal by Median-Type Noise Detectors and Detail-Preserving Regularization","volume":"14","author":"Chan","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_46","first-page":"45","article-title":"Image De-noising by Various Filters for Different Noise","volume":"9","author":"Patidar","year":"2010","journal-title":"Int. J. Comput. Appl."},{"key":"ref_47","first-page":"617","article-title":"Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques","volume":"3","author":"Verma","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Sci. Softw. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s42492-019-0016-7","article-title":"Brief Review of Image Denoising Techniques","volume":"2","author":"Fan","year":"2019","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/s42256-020-00273-z","article-title":"Deep Learning for Tomographic Image Reconstruction","volume":"2","author":"Wang","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"112903","DOI":"10.1117\/1.JBO.25.11.112903","article-title":"Deep Learning in Photoacoustic Tomography: Current Approaches and Future Directions","volume":"25","author":"Hauptmann","year":"2020","journal-title":"J. Biomed. Opt."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"100241","DOI":"10.1016\/j.pacs.2021.100241","article-title":"Deep Learning for Biomedical Photoacoustic Imaging: A Review","volume":"22","author":"Schellenberg","year":"2021","journal-title":"Photoacoustics"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"100215","DOI":"10.1016\/j.pacs.2020.100215","article-title":"Review of Deep Learning for Photoacoustic Imaging","volume":"21","author":"Yang","year":"2021","journal-title":"Photoacoustics"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"040901","DOI":"10.1117\/1.JBO.26.4.040901","article-title":"Deep Learning in Photoacoustic Imaging: A Review","volume":"26","author":"Deng","year":"2021","journal-title":"J. Biomed. Opt."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s13534-021-00210-y","article-title":"Photoacoustic Imaging Aided with Deep Learning: A Review","volume":"12","author":"Rajendran","year":"2022","journal-title":"Biomed. Eng. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Stylogiannis, A., Kousias, N., Kontses, A., Ntziachristos, L., and Ntziachristos, V. (2021). A Low-Cost Optoacoustic Sensor for Environmental Monitoring. Sensors, 21.","DOI":"10.3390\/s21041379"},{"key":"ref_56","first-page":"6303","article-title":"A Review of Rain Streaks Detection and Removal Techniques for Outdoor Single Image","volume":"11","author":"Shorman","year":"2016","journal-title":"ARPN J. Eng. Appl. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111101","DOI":"10.1007\/s11432-020-3225-9","article-title":"Survey on Rain Removal from Videos or a Single Image","volume":"65","author":"Wang","year":"2022","journal-title":"Sci. China Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13640-018-0275-9","article-title":"Weighted Median Guided Filtering Method for Single Image Rain Removal","volume":"2018","author":"Shi","year":"2018","journal-title":"Eurasip J. Image Video Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41592-018-0239-0","article-title":"Deep Learning Enables Cross-Modality Super-Resolution in Fluorescence Microscopy","volume":"16","author":"Wang","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3926","DOI":"10.1038\/s41598-019-40554-1","article-title":"Learning-Based Super-Resolution in Coherent Imaging Systems","volume":"9","author":"Liu","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1364\/BOE.8.003440","article-title":"Deep-Learning Based, Automated Segmentation of Macular Edema in Optical Coherence Tomography","volume":"8","author":"Lee","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6445","DOI":"10.1364\/BOE.409246","article-title":"Hybrid Deep Learning Network for Vascular Segmentation in Photoacoustic Imaging","volume":"11","author":"Yuan","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"14454","DOI":"10.1038\/s41598-019-51062-7","article-title":"A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head","volume":"9","author":"Devalla","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1364\/BOE.379551","article-title":"Noise Reduction in Optical Coherence Tomography Images Using a Deep Neural Network with Perceptually-Sensitive Loss Function","volume":"11","author":"Qiu","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_65","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_66","unstructured":"Vinod, N., and Geoffrey, H. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines Vinod Nair. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Nagi, J., Ducatelle, F., Di Caro, G.A., Ciresan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., and Gambardella, L.M. (2011, January 16\u201318). Max-Pooling Convolutional Neural Networks for Vision-Based Hand Gesture Recognition. Proceedings of the 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA.2011.6144164"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"34195","DOI":"10.1007\/s11042-020-09054-7","article-title":"Revisiting Spatial Dropout for Regularizing Convolutional Neural Networks","volume":"79","author":"Lee","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_69","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_71","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_73","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for Stochastic Optimization. arXiv."},{"key":"ref_74","unstructured":"Brochu, E., Cora, V.M., and de Freitas, N. (2010). A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2021.02.010","article-title":"Convolutional Neural Network with Median Layers for Denoising Salt-And-Pepper Contaminations","volume":"442","author":"Liang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Agrawal, S., Fadden, C., Dangi, A., Yang, X., Albahrani, H., Frings, N., Heidari Zadi, S., and Kothapalli, S.-R. (2019). Light-Emitting-Diode-Based Multispectral Photoacoustic Computed Tomography System. Sensors, 19.","DOI":"10.3390\/s19224861"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Francis, K.J., Booijink, R., Bansal, R., and Steenbergen, W. (2020). Tomographic Ultrasound and LED-Based Photoacoustic System for Preclinical Imaging. Sensors, 20.","DOI":"10.3390\/s20102793"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Bulsink, R., Kuniyil Ajith Singh, M., Xavierselvan, M., Mallidi, S., Steenbergen, W., and Francis, K.J. (2021). Oxygen Saturation Imaging Using LED-Based Photoacoustic System. Sensors, 21.","DOI":"10.3390\/s21010283"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3961\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:17:13Z","timestamp":1760138233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/10\/3961"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":80,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22103961"],"URL":"https:\/\/doi.org\/10.3390\/s22103961","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,23]]}}}