{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T11:09:29Z","timestamp":1774091369219,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:00:00Z","timestamp":1717113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["ZQN-1005"],"award-info":[{"award-number":["ZQN-1005"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["21BS118"],"award-info":[{"award-number":["21BS118"]}]},{"name":"Scientific Research Funds of Huaqiao University","award":["ZQN-1005"],"award-info":[{"award-number":["ZQN-1005"]}]},{"name":"Scientific Research Funds of Huaqiao University","award":["21BS118"],"award-info":[{"award-number":["21BS118"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low-Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during the reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image\u2019s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5 dB and 0.2, respectively.<\/jats:p>","DOI":"10.3390\/s24113560","type":"journal-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T11:43:48Z","timestamp":1717155828000},"page":"3560","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Residual Dense Attention Generative Adversarial Network for Microscopic Image Super-Resolution"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4860-3500","authenticated-orcid":false,"given":"Sanya","family":"Liu","sequence":"first","affiliation":[{"name":"Xiamen Key Laboratory of Mobile Multimedia Communications, College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Weng","sequence":"additional","affiliation":[{"name":"Xiamen Key Laboratory of Mobile Multimedia Communications, College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7385-5825","authenticated-orcid":false,"given":"Xingen","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoxin","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Microelectronics Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5137-7858","authenticated-orcid":false,"given":"Lin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Xiamen Key Laboratory of Mobile Multimedia Communications, College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e634","DOI":"10.1002\/cpz1.634","article-title":"Multi-Photon Microscopy","volume":"3","author":"Sanderson","year":"2023","journal-title":"Curr. Protoc."},{"key":"ref_2","first-page":"317","article-title":"Multiframe Image Restoration and Registration","volume":"1","author":"Tsai","year":"1984","journal-title":"Adv. Comput. Vis. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1021\/acsphotonics.8b00146","article-title":"Deep Learning Enhanced Mobile-Phone Microscopy","volume":"5","author":"Rivenson","year":"2018","journal-title":"ACS Photonics"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1038\/nbt.4106","article-title":"Deep Learning Massively Accelerates Super-Resolution Localization Microscopy","volume":"36","author":"Wei","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"ref_5","first-page":"458","article-title":"Deep-STORM: Super-Resolution Single-Molecule Microscopy by Deep Learning","volume":"5","author":"Nehme","year":"2018","journal-title":"Optics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.inffus.2023.03.021","article-title":"From Degrade to Upgrade: Learning a Self-Supervised Degradation Guided Adaptive Network for Blind Remote Sensing Image Super-Resolution","volume":"96","author":"Xiao","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_7","first-page":"5610819","article-title":"Satellite Video Super-Resolution via Multiscale Deformable Convolution Alignment and Temporal Grouping Projection","volume":"60","author":"Xiao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.1109\/TCSVT.2023.3312321","article-title":"Local-Global Temporal Difference Learning for Satellite Video Super-Resolution","volume":"34","author":"Xiao","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/TIP.2004.826093","article-title":"Linear Interpolation Revitalized","volume":"13","author":"Blu","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1109\/TIP.2003.811493","article-title":"A Note on Cubic Convolution Interpolation","volume":"12","author":"Meijering","year":"2003","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fan, C., Wu, C., Li, G., and Ma, J. (2017). Projections onto Convex Sets Super-Resolution Reconstruction Based on Point Spread Function Estimation of Low-Resolution Remote Sensing Images. Sensors, 17.","DOI":"10.3390\/s17020362"},{"key":"ref_12","first-page":"237","article-title":"Super-Resolution Image Reconstruction Based on an Improved Maximum a Posteriori Algorithm","volume":"27","author":"Li","year":"2018","journal-title":"J. Beijing Inst. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image Super-Resolution Via Sparse Representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (2016, January 27\u201330). Accurate Image Super-Resolution Using Very Deep Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., and Yang, M.H. (2017, January 21\u201326). Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-Realistic Single Image Super Resolution Using a Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced Deep Residual Networks for Single Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y.R. (2018, January 18\u201323). Residual Dense Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1364\/BOE.10.001044","article-title":"High-Throughput, High-Resolution Deep Learning Microscopy Based on Registration-Free Generative Adversarial Network","volume":"10","author":"Zhang","year":"2019","journal-title":"Biomed. Opt. Express"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1109\/TMI.2020.3037790","article-title":"Super-Resolution Ultrasound Localization Microscopy Through Deep Learning","volume":"40","author":"Solomon","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1038\/s41592-020-01048-5","article-title":"Evaluation and Development of Deep Neural Networks for Image Super-Resolution in Optical Microscopy","volume":"18","author":"Qiao","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Park, S.J., Son, H., Cho, S., Hong, K.S., and Lee, S. (2018, January 8\u201314). SRFeat: Single Image Super-Resolution with Feature Discrimination. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01270-0_27"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., and Loy, C.C. (2018, January 8\u201314). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y.R. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3560\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:51:59Z","timestamp":1760107919000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,31]]},"references-count":27,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113560"],"URL":"https:\/\/doi.org\/10.3390\/s24113560","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,31]]}}}