{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T10:02:43Z","timestamp":1769853763110,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T00:00:00Z","timestamp":1747440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The photon detection capability of quanta image sensors make them an optimal choice for low-light imaging. To address Possion noise in QIS reconstruction caused by spatio-temporal oversampling characteristic, a deep learning-based noise suppression reconstruction method is proposed in this paper. The proposed neural network integrates convolutional neural networks and Transformers. Its architecture combines the Anscombe transformation with serial and parallel modules to enhance denoising performance and adaptability across various scenarios. Experimental results demonstrate that the proposed method effectively suppresses noise in QIS image reconstruction. Compared with representative methods such as TD-BM3D, QIS-Net and DPIR, our approach achieves up to 1.2 dB improvement in PSNR, demonstrating superior reconstruction quality.<\/jats:p>","DOI":"10.3390\/jimaging11050160","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T03:51:03Z","timestamp":1747626663000},"page":"160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Guanjie","family":"Wang","sequence":"first","affiliation":[{"name":"School of Microelectronics, Tianjin University, 92 Weijin Road, Tianjin 300072, China"}]},{"given":"Zhiyuan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, 92 Weijin Road, Tianjin 300072, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fossum, E.R. (2017). Some Thoughts on Future Digital Still Cameras. Image Sensors and Signal Processing for Digital Still Cameras, CRC Press.","DOI":"10.1201\/9781420026856-11"},{"key":"ref_2","unstructured":"Fossum, E.R. (2005, January 9\u201311). What to do with sub-diffraction-limit (SDL) pixels?\u2014A proposal for a gigapixel digital film sensor (DFS). Proceedings of the IEEE Workshop on Charge-Coupled Devices and Advanced Image Sensors, Nagano, Japan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fossum, E.R. (2011, January 10\u201314). The quanta image sensor (QIS): Concepts and challenges. Proceedings of the Imaging and Applied Optics, Toronto, ON, Canada.","DOI":"10.1364\/COSI.2011.JTuE1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"891","DOI":"10.1109\/LED.2021.3072842","article-title":"A 0.19 e-rms read noise 16.7 Mpixel stacked quanta image sensor with 1.1 \u03bcm-pitch backside illuminated pixels","volume":"42","author":"Ma","year":"2021","journal-title":"IEEE Electron. Device Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2824","DOI":"10.1109\/TED.2022.3166716","article-title":"Review of quanta image sensors for ultralow-light imaging","volume":"69","author":"Ma","year":"2022","journal-title":"IEEE Trans. Electron. Devices"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, C., Qu, X., Gnanasambandam, A., Elgendy, O.A., Ma, J., and Chan, S.H. (2021, January 11\u201317). Photon-Limited Object Detection Using Non-Local Feature Matching and Knowledge Distillation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00443"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1109\/TCI.2020.3041093","article-title":"HDR Imaging with Quanta Image Sensors: Theoretical Limits and Optimal Reconstruction","volume":"6","author":"Gnanasambandam","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gyongy, I., Dutton, N.A., and Henderson, R.K. (2018). Single-Photon Tracking for High-Speed Vision. Sensors, 18.","DOI":"10.3390\/s18020323"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TCI.2020.2964238","article-title":"Color Filter Arrays for Quanta Image Sensors","volume":"6","author":"Elgendy","year":"2020","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_10","unstructured":"Chen, S., Ceballos, A., and Fossum, E.R. (2013, January 12\u201316). Digital integration sensor. Proceedings of the International Image Sensor Workshop, Snowbird, UT, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fossum, E.R., Ma, J., and Masoodian, S. (2016, January 5). Quanta image sensor: Concepts and progress. Proceedings of the Advanced Photon Counting Techniques X, Baltimore, MD, USA.","DOI":"10.1117\/12.2227179"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/JEDS.2013.2284054","article-title":"Modeling the Performance of Single-Bit and Multi-Bit Quanta Image Sensors","volume":"1","author":"Fossum","year":"2013","journal-title":"IEEE J. Electron. Devices Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/JEDS.2016.2536722","article-title":"Photon Counting Error Rates in Single-Bit and Multi-Bit Quanta Image Sensors","volume":"4","author":"Fossum","year":"2016","journal-title":"IEEE J. Electron. Devices Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1109\/TCI.2022.3187657","article-title":"Exposure-Referred Signal-to-Noise Ratio for Digital Image Sensors","volume":"8","author":"Gnanasambandam","year":"2022","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1109\/TIP.2011.2179306","article-title":"Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics","volume":"21","author":"Yang","year":"2012","journal-title":"IIEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chan, S.H., and Lu, Y.M. (2014, January 3\u20135). Efficient Image Reconstruction for Gigapixel Quantum Image Sensors. Proceedings of the 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Atlanta, GA, USA.","DOI":"10.1109\/GlobalSIP.2014.7032129"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chan, S.H., Elgendy, O.A., and Wang, X. (2016). Images from Bits: Non-Iterative Image Reconstruction for Quanta Image Sensors. Sensors, 16.","DOI":"10.3390\/s16111961"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Choi, J.H., Elgendy, O.A., and Chan, S.H. (2018, January 15\u201320). Image Reconstruction for Quanta Image Sensors Using Deep Neural Networks. Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada.","DOI":"10.1109\/ICASSP.2018.8461685"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.neunet.2020.07.025","article-title":"Deep Learning on Image Denoising: An Overview","volume":"131","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1137\/090773908","article-title":"Image Denoising Methods. A New Nonlocal Principle","volume":"52","author":"Buades","year":"2010","journal-title":"SIAM Rev."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Andriyanov, N., Belyanchikov, A., Vasiliev, K., and Dementiev, V. (2022, January 18\u201320). Restoration of Spatially Inhomogeneous Images Based on Doubly Stochastic Filters. Proceedings of the 2022 IEEE International Conference on Information Technologies (ITNT), Moscow, Russia.","DOI":"10.1109\/ITNT55410.2022.9848684"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Krasheninnikov, V., Kuvayskova, Y., and Subbotin, A. (2020, January 23\u201327). Pseudo-gradient Algorithm for Identification of Doubly Stochastic Cylindrical Image Model. Proceedings of the 2020 International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia.","DOI":"10.1016\/j.procs.2020.09.225"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103013","DOI":"10.1016\/j.inffus.2025.103013","article-title":"Efficient Image Denoising Using Deep Learning: A Brief Survey","volume":"118","author":"Jiang","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhao, X., Han, L., Nie, K., Xu, L., and Ma, J. (2018). Effect of the Transition Points Mismatch on Quanta Image Sensors. Sensors, 18.","DOI":"10.3390\/s18124357"},{"key":"ref_25","unstructured":"Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001, January 7\u201314). A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proceedings of the Eighth IEEE International Conference on Computer Vision, Vancouver, BC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van Gool, L., Yang, M.H., and Zhang, L. (2017, January 21\u201326). NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TIP.2016.2631888","article-title":"Waterloo Exploration Database: New Challenges for Image Quality Assessment Models","volume":"26","author":"Ma","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","unstructured":"Roth, S., and Black, M.J. (2005, January 20\u201325). Fields of Experts: A Framework for Learning Image Priors. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_30","unstructured":"Franzen, R. (2023, March 18). Kodak Lossless True Color Image Suite. Available online: http:\/\/r0k.us\/graphics\/kodak."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6360","DOI":"10.1109\/TPAMI.2021.3088914","article-title":"Plug-and-Play Image Restoration with Deep Denoiser Prior","volume":"44","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"083102","DOI":"10.1117\/1.OE.63.8.083102","article-title":"Motion Deblurring Method of Quanta Image Sensor Based on Spatial Correlation and Frequency Domain Characteristics","volume":"63","author":"Wang","year":"2024","journal-title":"Opt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, X., Hu, E., Wang, A., Shiri, B., and Lin, W. (2025). VNDHR: Variational Single Nighttime Image Dehazing for Enhancing Visibility in Intelligent Transportation Systems via Hybrid Regularization. IEEE Trans. Intell. Transp. Syst., 1\u201315.","DOI":"10.1109\/TITS.2025.3550267"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:34:33Z","timestamp":1760031273000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,17]]},"references-count":33,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["jimaging11050160"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11050160","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,17]]}}}