{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:10:52Z","timestamp":1760145052627,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:00:00Z","timestamp":1718409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, underwater imaging and vision technologies have received widespread attention, and the removal of the backward-scattering interference caused by impurities in the water has become a long-term research focus for scholars. With the advent of new single-photon imaging devices, single-photon avalanche diode (SPAD) devices, with high sensitivity and a high depth resolution, have become cutting-edge research tools in the field of underwater imaging. However, the high production costs and small array areas of SPAD devices make it very difficult to conduct underwater SPAD imaging experiments. To address this issue, we propose a fast and effective underwater SPAD data simulation method and develop a denoising network for the removal of backward-scattering interference in underwater SPAD images based on deep learning and simulated data. The experimental results show that the distribution difference between the simulated and real underwater SPAD data is very small. Moreover, the algorithm based on deep learning and simulated data for the removal of backward-scattering interference in underwater SPAD images demonstrates effectiveness in terms of both metrics and human observation. The model yields improvements in metrics such as the PSNR, SSIM, and entropy of 5.59 dB, 9.03%, and 0.84, respectively, demonstrating its superior performance.<\/jats:p>","DOI":"10.3390\/s24123886","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T06:29:43Z","timestamp":1718605783000},"page":"3886","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Simulation Method for Underwater SPAD Depth Imaging Datasets"],"prefix":"10.3390","volume":"24","author":[{"given":"Taoran","family":"Lu","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-3044","authenticated-orcid":false,"given":"Su","family":"Qiu","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shihao","family":"Zhu","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqi","family":"Jin","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, S., and Liu, T. (2024). Underwater rescue target detection based on acoustic images. Sensors, 24.","DOI":"10.3390\/s24061780"},{"key":"ref_2","first-page":"1269","article-title":"Algorithm for de-scattering 3D images from infrared time-of-flight (ToF) cameras","volume":"13104","author":"Lu","year":"2024","journal-title":"Adv. Fiber Laser Conf."},{"key":"ref_3","first-page":"72","article-title":"Reconstruction of high-resolution depth profiling from single-photon data based on PCA","volume":"12772","author":"Wang","year":"2023","journal-title":"Real-Time Photonic Meas. Data Manag. Process. VII"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4590","DOI":"10.1364\/OE.27.004590","article-title":"Three-dimensional single-photon imaging through obscurants","volume":"27","author":"Tobin","year":"2019","journal-title":"Opt. Express"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"28437","DOI":"10.1364\/OE.27.028437","article-title":"Three-dimensional imaging of stationary and moving targets in turbid underwater environments using a single-photon detector array","volume":"27","author":"Maccarone","year":"2019","journal-title":"Opt. Express"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16690","DOI":"10.1364\/OE.487129","article-title":"Submerged single-photon LiDAR imaging sensor used for real-time 3D scene reconstruction in scattering underwater environments","volume":"31","author":"Maccarone","year":"2023","journal-title":"Opt. Express"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7300209","DOI":"10.1109\/JPHOT.2021.3113659","article-title":"Real-time and high-speed underwater photon-counting communication based on SPAD and PPM symbol synchronization","volume":"13","author":"Huang","year":"2021","journal-title":"IEEE Photonics J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Panglosse, A., Martin-Gonthier, P., Marcelot, O., Virmontois, C., Saint-P\u00e9, O., and Magnan, P. (2021). Modeling, simulation methods and characterization of photon detection probability in cmos-spad. Sensors, 21.","DOI":"10.3390\/s21175860"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7380","DOI":"10.1364\/AO.498189","article-title":"Design and simulation of a near-infrared enhanced Si-based SPAD for an automotive LiDAR","volume":"62","author":"Xie","year":"2023","journal-title":"Appl. Opt."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/JEDS.2019.2895151","article-title":"A simple analytic modeling method for SPAD timing jitter prediction","volume":"7","author":"Sun","year":"2019","journal-title":"IEEE J. Electron. Devices Soc."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hu, M., and Zhang, K. (2024). Underwater Turbid Media Stokes-Based Polarimetric Recovery. Sensors, 24.","DOI":"10.3390\/s24051367"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/48.50695","article-title":"Computer modeling and the design of optimal underwater imaging systems","volume":"15","author":"Jaffe","year":"1990","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, Y., Zhang, T., Yang, K., Zhang, J., and Wang, X. (2024). A Study on Refraction Error Compensation Method for Underwater Spinning Laser Scanning Three-Dimensional Imaging. Sensors, 24.","DOI":"10.3390\/s24020343"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Scholes, S., Mora-Mart\u00edn, G., Zhu, F., Gyongy, I., Soan, P., and Leach, J. (2022). Simulating single-photon detector array sensors for depth imaging. arXiv.","DOI":"10.21203\/rs.3.rs-2238499\/v1"},{"key":"ref_15","first-page":"50","article-title":"Analysis of diffuse light field based on Monte Carlo simulation","volume":"37","author":"Chen","year":"2016","journal-title":"Laser J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xin, Y.L., Ge, G.P., Du, W., Wu, H., and Zhao, Y. (2024). Design of an Optical Physics Virtual Simulation System Based on Unreal Engine 5. Appl. Sci., 14.","DOI":"10.3390\/app14030955"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5187","DOI":"10.1109\/TIP.2016.2598681","article-title":"Dehazenet: An end-to-end system for single image haze removal","volume":"25","author":"Cai","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, C., Wei, Y., Guo, K., and Han, H. (2024). Removal of Mixed Noise in Hyperspectral Images Based on Subspace Representation and Nonlocal Low-Rank Tensor Decomposition. Sensors, 24.","DOI":"10.3390\/s24020327"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8","DOI":"10.4236\/jcc.2019.73002","article-title":"Image quality assessment through FSIM, SSIM, MSE and PSNR\u2014a comparative study","volume":"7","author":"Sara","year":"2019","journal-title":"J. Comput. Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1007\/s10278-007-9044-5","article-title":"Information entropy measure for evaluation of image quality","volume":"21","author":"Tsai","year":"2008","journal-title":"J. Digit. Imaging"},{"key":"ref_21","first-page":"304","article-title":"Algorithm for image processing using improved median filter and comparison of mean, median and improved median filter","volume":"1","author":"Gupta","year":"2011","journal-title":"Int. J. Soft Comput. Eng. (IJSCE)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1109\/TIP.2011.2176954","article-title":"BM3D frames and variational image deblurring","volume":"21","author":"Danielyan","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3886\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:59:21Z","timestamp":1760108361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3886"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,15]]},"references-count":23,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123886"],"URL":"https:\/\/doi.org\/10.3390\/s24123886","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,6,15]]}}}