{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:32:15Z","timestamp":1778603535356,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T00:00:00Z","timestamp":1743206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Generalitat Valenciana, Conselleria de Educaci\u00f3n, Cultura, Universidades y Empleo (Spain)","award":["CIGE\/2023\/52"],"award-info":[{"award-number":["CIGE\/2023\/52"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Super-resolution (SR) techniques have gained traction in biomedical imaging for their ability to enhance image quality. However, it remains unclear whether these improvements translate into better performance in clinical tasks. In this study, we provide a comprehensive evaluation of state-of-the-art SR models\u2014including CNN- and Transformer-based architectures\u2014by assessing not only visual quality metrics (PSNR and SSIM) but also their downstream impact on segmentation and classification performance for lung CT scans. Using U-Net and ResNet architectures, we quantify how SR influences diagnostic tasks across different datasets, and we evaluate model generalization in cross-domain settings. Our findings show that advanced SR models such as SwinIR preserve diagnostic features effectively and, when appropriately applied, can enhance or maintain clinical performance even in low-resolution contexts. This work bridges the gap between image quality enhancement and practical clinical utility, providing actionable insights for integrating SR into real-world biomedical imaging workflows.<\/jats:p>","DOI":"10.3390\/jimaging11040104","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T01:59:36Z","timestamp":1743386376000},"page":"104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluating Super-Resolution Models in Biomedical Imaging: Applications and Performance in Segmentation and Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6502-8225","authenticated-orcid":false,"given":"Mario","family":"Amoros","sequence":"first","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente del Raspeig, Ap. Correos 99, E-03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2307-1760","authenticated-orcid":false,"given":"Manuel","family":"Curado","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente del Raspeig, Ap. Correos 99, E-03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2990-1879","authenticated-orcid":false,"given":"Jose F.","family":"Vicent","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente del Raspeig, Ap. Correos 99, E-03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Izonin, I., Tkachenko, R., Peleshko, D., Rak, T., and Batyuk, D. (2015, January 14\u201317). Learning-Based Image Super-Resolution Using Weight Coefficients of Synaptic Connections. Proceedings of the 2015 Xth International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine.","DOI":"10.1109\/STC-CSIT.2015.7325423"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, R., Xiao, C., Huang, Y., Hassan, H., and Huang, B. (2022). Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020298"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s10278-017-0033-z","article-title":"Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT","volume":"31","author":"Umehara","year":"2018","journal-title":"J. Digit. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Georgescu, M.I., Ionescu, R., Miron, A.I., Savencu, O., Ristea, N.C., Verga, N., and Khan, F. (2022). Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution. arXiv.","DOI":"10.1109\/WACV56688.2023.00223"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.irbm.2020.08.004","article-title":"A Review of the Deep Learning Methods for Medical Images Super Resolution Problems","volume":"42","author":"Li","year":"2021","journal-title":"IRBM"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1148\/radiol.12111607","article-title":"Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data\u2013Methods and Preliminary Results","volume":"264","author":"Gevaert","year":"2012","journal-title":"Radiology"},{"key":"ref_8","unstructured":"Napel, S. (2014). NSCLC Radiogenomics: Initial Stanford Study of 26 Cases, The Cancer Imaging Archive."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"180160","DOI":"10.1038\/sdata.2018.202","article-title":"A Radiogenomic Dataset of Non-Small Cell Lung Cancer","volume":"5","author":"Bakr","year":"2018","journal-title":"Sci. Data"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hor\u00e9, A., and Ziou, D. (2010, January 23\u201326). Image Quality Metrics: PSNR vs. SSIM. In Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_11","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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (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"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Deka, B., Datta, S., Mullah, H., and Hazarika, S. (2020). Diffusion-weighted and spectroscopic MRI super-resolution using sparse representations. Biomed. Signal Process. Control, 60.","DOI":"10.1016\/j.bspc.2020.101941"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Amoros, M., Curado, M., and Vicent, J.F. (2024, January 1). CTextureFusion: Advanced Texture Transfer with Multi-head Attention for Improving Lung CT Super Resolution. Proceedings of International Conference on Pattern Recognition 2024, Kalkota, India.","DOI":"10.1007\/978-3-031-78195-7_7"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","article-title":"Deep Learning for Image Super-Resolution: A Survey","volume":"43","author":"Wang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, Z., Lin, Z., and Qi, H. (2019). Image Super-Resolution by Neural Texture Transfer. arXiv.","DOI":"10.1109\/CVPR.2019.00817"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15187","DOI":"10.1080\/10106049.2022.2096699","article-title":"Super-Resolution Reconstruction of GOSAT CO2 Products Using Bicubic Interpolation","volume":"37","author":"Xiang","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11604-018-0796-2","article-title":"Improvement of Image Quality at CT and MRI Using Deep Learning","volume":"37","author":"Higaki","year":"2019","journal-title":"Jpn. J. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C., He, K., and Tang, X. (2014, January 6\u201312). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the Computer Vision\u2014ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_20","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Loy, C., Qiao, Y., and Tang, X. (2025, January 16). ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Available online: https:\/\/github.com\/xinntao\/ESRGAN."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 11\u201317). SwinIR: Image Restoration Using Swin Transformer. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, L., Zeng, X., Huang, Z., Li, W., and Zhang, H. (2020). Low-Dose Chest X-Ray Image Super-Resolution Using Generative Adversarial Nets with Spectral Normalization. Biomed. Signal Process. Control, 55.","DOI":"10.1016\/j.bspc.2019.101600"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhou, Z., Liao, G., and Yuan, K. (2020, January 4). Csrgan: Medical Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the ISBI Workshops 2020\u2014International Symposium on Biomedical Imaging Workshops, Iowa City, IA, USA.","DOI":"10.1109\/ISBIWorkshops50223.2020.9153436"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","article-title":"FSIM: A Feature Similarity Index for Image Quality Assessment","volume":"20","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1007\/s10278-022-00721-9","article-title":"Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging\u2014State-of-the-Art and Challenges","volume":"36","author":"Chen","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Loizidou, K., Elia, R., and Pitris, C. (2023). Computer-Aided Breast Cancer Detection and Classification in Mammography: A Comprehensive Review. Comput. Biol. Med., 153.","DOI":"10.1016\/j.compbiomed.2023.106554"},{"key":"ref_27","unstructured":"Dong, C., Loy, C., He, K., and Tang, X. (2025, January 16). Image Super-Resolution Using Deep Convolutional Networks. Available online: http:\/\/mmlab.ie.cuhk.edu.hk\/."},{"key":"ref_28","unstructured":"Ma, J., Ge, C., Wang, Y., An, X., Gao, J., Yu, Z., Zhang, M., Liu, X., Deng, X., and Cao, S. (2024, November 10). COVID-19 CT Lung and Infection Segmentation Dataset. Available online: https:\/\/doi.org\/10.5281\/zenodo.3757476."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:05:21Z","timestamp":1760029521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/4\/104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,29]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["jimaging11040104"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11040104","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,29]]}}}