{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:01:09Z","timestamp":1760238069977,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,7]],"date-time":"2022-06-07T00:00:00Z","timestamp":1654560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Natural Science Foundation of China","award":["62071229","51875289","61873124","2020Z060052001","20182952029","NJ2020014","NS2019017"],"award-info":[{"award-number":["62071229","51875289","61873124","2020Z060052001","20182952029","NJ2020014","NS2019017"]}]},{"name":"The Aeronautical Science Foundation of China","award":["62071229","51875289","61873124","2020Z060052001","20182952029","NJ2020014","NS2019017"],"award-info":[{"award-number":["62071229","51875289","61873124","2020Z060052001","20182952029","NJ2020014","NS2019017"]}]},{"name":"The Fundamental Research Funds for the Central Universities","award":["62071229","51875289","61873124","2020Z060052001","20182952029","NJ2020014","NS2019017"],"award-info":[{"award-number":["62071229","51875289","61873124","2020Z060052001","20182952029","NJ2020014","NS2019017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>PET (Positron Emission Computed Tomography) imaging is a challenge due to the ill-posed nature and the low data of photo response lines. Generative adversarial networks have been widely used in computer vision and made great success recently. In our paper, we trained an adversarial model to improve the industrial positron images quality based on the attention mechanism. The innovation of the proposed method is that we build a memory module that focuses on the contribution of feature details to interested parts of images. We use an encoder to get the hidden vectors from a basic dataset as the prior knowledge and train the nets jointly. We evaluate the quality of the simulation positron images by MS-SSIM and PSNR. At the same time, the real industrial positron images also show a good visual effect.<\/jats:p>","DOI":"10.3390\/e24060793","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:22:39Z","timestamp":1654820559000},"page":"793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative Adversarial Network of Industrial Positron Images on Memory Module"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6144-9643","authenticated-orcid":false,"given":"Mingwei","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruipeng","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,7]]},"reference":[{"key":"ref_1","first-page":"2672","article-title":"Advances in neural information processing systems","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Curran Assoc."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A., Isola, P., Zhu, J.Y., and Zhou, T. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Park, S.J., Son, H., and Cho, 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_4","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T.S. (2018, January 18\u201323). Generative Image Inpainting with Contextual Attention. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00577"},{"key":"ref_6","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Image and Graphics, Springer."},{"key":"ref_7","first-page":"214","article-title":"Wasserstein GAN","volume":"70","author":"Arjovsky","year":"2017","journal-title":"Int. Conf. Mach. Learn."},{"key":"ref_8","first-page":"2672","article-title":"Conditional Generative Adversarial Nets","volume":"1411","author":"Mirza","year":"2014","journal-title":"Comput. Sci."},{"key":"ref_9","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., and Zhang, L. (2017, January 21\u201326). Learning deep CNN denoiser prior for image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.300"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","article-title":"Attention-guided CNN for image denoising","volume":"124","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chuquicusma, M., Hussein, S., Burt, J., and Bagci, U. (2018, January 4\u20137). How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging, Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363564"},{"key":"ref_13","unstructured":"Kitchen, A., and Seah, J. (2017). Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seebck, P., Waldstein, S.M., Schmidt-Erfurth, U., and Langs, G. (2017, January 25\u201330). Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. Proceedings of the 2017 International Conference on Information Processing in Medical Imaging, Boone, NC, USA.","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Baur, C., Albarqouni, S., and Navab, N. (2018, January 16). Generating Highly Realistic Images of Skin Lesions with GANs. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-01201-4_28"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wei, W., Poirion, E., Bodini, B., Durrleman, S., Ayache, N., Stankoff, B., and Colliot, O. (2018, January 16). Learning myelin content in multiple sclerosis from multimodal MRI through adversarial training. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00931-1_59"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tom, F., and Sheet, D. (2018, January 4\u20137). Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning. Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363780"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1109\/TMI.2017.2781228","article-title":"Adversarial Stain Transfer for Histopathology Image Analysis","volume":"37","author":"Bentaieb","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_19","unstructured":"Wolterink, J.M., Leiner, T., and Isgum, I. (2018). Blood Vessel Geometry Synthesis using Generative Adversarial Networks. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Lin, Y., Dong, C., Loy, C.C., Qiao, Y., and Tang, X. (2018, January 8\u201314). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, 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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_22","unstructured":"Jolicoeur-Martineau, A. (2018). The relativistic discriminator: A key element missing from standard GAN. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dong, N., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., and Shen, D. (2017). Medical Image Synthesis with Context-Aware Generative Adversarial Networks. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-70004-5"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2019.11.063","article-title":"Multi-stream attentive generative adversarial network for dynamic scene deblurring","volume":"383","author":"Cui","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Denck, J., Guehring, J., Maier, A., and Rothgang, E. (2021). Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks. arXiv.","DOI":"10.3390\/jimaging7080133"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s12539-020-00385-5","article-title":"Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks","volume":"12","author":"Yang","year":"2020","journal-title":"Interdiscip Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","article-title":"PCANet: A Simple Deep Learning Baseline for Image Classification?","volume":"24","author":"Chan","year":"2015","journal-title":"IEEE Trans. Image Proc."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Matten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_29","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_30","unstructured":"Odena, A., Olah, C., and Shlens, J. (2017, January 6\u201311). Conditional Image Synthesis with Auxiliary Classifier GANs. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_31","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/6\/793\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:25:22Z","timestamp":1760138722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/6\/793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,7]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["e24060793"],"URL":"https:\/\/doi.org\/10.3390\/e24060793","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2022,6,7]]}}}