{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:02:02Z","timestamp":1777982522034,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This work introduces an image quality assessment (IQA)-driven optimization framework for synthetic data generation (SDG) to improve the simulation-to-real-world (sim2real) transfer performance of synthetic vision systems. By using IQA metrics\u2014particularly structural similarity (SSIM) based\u2014as fitness functions for genetic algorithms (GAs), we automate the tuning of synthetic environments to enhance the generalizability of synthetic-trained object detectors in new or unobserved real-world scenarios. Digital-twin environments were constructed using both 3D Gaussian splatting from real images and by procedural generation methods. Two GAs were developed: a pre-process GA that modifies the environment before image generation for background focused similarity, and a post-process GA that adjusts noise and visual properties in the synthetic images, focusing on foreground-object similarity. The Synthetic Image Quality Analysis Calculator (SIQAC) software was extended to define regions of interest and evaluate transferability. Experimental results show sim2real accuracy improvements of up to 36% with the pre-process GA, 19% with the post-process GA, and 33% when combined, reaching near real vision system performance levels. These findings demonstrate the benefits of IQA-informed optimization in reducing the sim2real gap for synthetic vision systems.<\/jats:p>","DOI":"10.1007\/s00371-025-04230-y","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T06:11:41Z","timestamp":1768889501000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Foreground and background image quality optimization for improved sim2real transfer in synthetic vision systems"],"prefix":"10.1007","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9861-244X","authenticated-orcid":false,"given":"Michael A.","family":"Mardikes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John T.","family":"Evans","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nathan C.","family":"Sprague","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"4230_CR1","unstructured":"Akunuru, S.S.: 3D Digital Twin Representation of Building Indoors. Master\u2019s thesis, The University of Texas at San Antonio, United States \u2013 Texas (2022). https:\/\/www.proquest.com\/docview\/2754033998\/abstract\/BE7B58C557C94240PQ\/1. ISBN: 9798358492257"},{"key":"4230_CR2","unstructured":"Vatanen, J.: EXPLORING NVIDIA OMNIVERSE ECOSYSTEM. Ph.D. thesis, Oulu University of Applied Sciences (2024)"},{"key":"4230_CR3","doi-asserted-by":"crossref","unstructured":"Sim, J.\u00a0K., et\u00a0al.: Designing an Educational Metaverse: A Case Study of NTUniverse. Applied Sciences 14, 2559 (2024). https:\/\/www.mdpi.com\/2076-3417\/14\/6\/2559. Number: 6 Publisher: Multidisciplinary Digital Publishing Institute","DOI":"10.3390\/app14062559"},{"key":"4230_CR4","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/978-3-319-38756-7_4","volume-title":"Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches","author":"M Grieves","year":"2017","unstructured":"Grieves, M., Vickers, J.: in Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, F.-J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, pp. 85\u2013113. Springer International Publishing, Cham (2017)"},{"key":"4230_CR5","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4271\/2016-01-0128","volume":"4","author":"P Koopman","year":"2016","unstructured":"Koopman, P., Wagner, M.: Challenges in autonomous vehicle testing and validation. SAE Int J. Transport. Safety 4, 15\u201324 (2016)","journal-title":"SAE Int J. Transport. Safety"},{"key":"4230_CR6","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1177\/00375497241261412","volume":"101","author":"CR Hudson","year":"2025","unstructured":"Hudson, C.R., et al.: Multi-domain modeling of environment and ecosystem of virtual off-road scenes for simulating ground vehicle autonomy. Simulation 101, 29\u201339 (2025). https:\/\/doi.org\/10.1177\/00375497241261412","journal-title":"Simulation"},{"key":"4230_CR7","doi-asserted-by":"publisher","first-page":"518","DOI":"10.1007\/978-3-030-64559-5_41","volume-title":"Advances in Visual Computing","author":"M Mousavi","year":"2020","unstructured":"Mousavi, M., Khanal, A., Estrada, R., Bebis, G., et al.: AI playground: unreal engine-based data ablation tool for deep learning. In: Bebis, G., et al. (eds.) Advances in Visual Computing, pp. 518\u2013532. Springer International Publishing, Cham (2020)"},{"key":"4230_CR8","doi-asserted-by":"crossref","unstructured":"Martinez-Gonzalez, P., et\u00a0al.: Unrealrox+: an improved tool for acquiring synthetic data from virtual 3d environments. IEEE, China, pp. 1\u20138 (2021)","DOI":"10.1109\/IJCNN52387.2021.9534447"},{"key":"4230_CR9","doi-asserted-by":"publisher","unstructured":"Qiu, W., et\u00a0al.: UnrealCV: virtual worlds for computer vision. Proceedings of the 25th ACM international conference on Multimedia 1221\u20131224 (2017). https:\/\/doi.org\/10.1145\/3123266.3129396","DOI":"10.1145\/3123266.3129396"},{"key":"4230_CR10","doi-asserted-by":"publisher","unstructured":"Seif, R.\u00a0J.: A Multi-fidelity approach to testing and evaluation of AI-enabled systems. Ph.D. thesis, Purdue University Graduate School (2024). https:\/\/doi.org\/10.25394\/PGS.26364277.v1","DOI":"10.25394\/PGS.26364277.v1"},{"key":"4230_CR11","doi-asserted-by":"crossref","unstructured":"Mardikes, M., Evans, J., Sprague, N., Shaver, G.: Digital twin fidelity estimate by image quality analysis for simulation-to-real-world transferability (2025)","DOI":"10.36227\/techrxiv.173933581.11924651\/v2"},{"key":"4230_CR12","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"4230_CR13","doi-asserted-by":"crossref","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering (2023). ArXiv:2308.04079 [cs]","DOI":"10.1145\/3592433"},{"key":"4230_CR14","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., et\u00a0al.: NeRF: Representing Scenes as neural radiance fields for view synthesis (2020). ArXiv:2003.08934 [cs]","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"4230_CR15","unstructured":"Chen, G., Wang, W.: A survey on 3D gaussian splatting (2024). ArXiv:2401.03890 [cs]"},{"key":"4230_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.104928","volume":"152","author":"X Kong","year":"2023","unstructured":"Kong, X., Hucks, R.G.: Preserving our heritage: a photogrammetry-based digital twin framework for monitoring deteriorations of historic structures. Automation Construct. 152, 104928 (2023)","journal-title":"Automation Construct."},{"key":"4230_CR17","doi-asserted-by":"publisher","unstructured":"Mardikes, M., et\u00a0al.: Constructing digital-twin roadways for testing and evaluation of autonomous roadside mowing vehicles (2025). https:\/\/doi.org\/10.36227\/techrxiv.173687650.09495419\/v1","DOI":"10.36227\/techrxiv.173687650.09495419\/v1"},{"key":"4230_CR18","first-page":"679","volume":"3667","author":"MK Kwak","year":"1999","unstructured":"Kwak, M.K., Shin, T.-S.: Real-time automatic tuning of vibration controllers for smart structures by genetic algorithm. SPIE 3667, 679\u2013690 (1999)","journal-title":"SPIE"},{"key":"4230_CR19","doi-asserted-by":"crossref","unstructured":"Lambora, A., Gupta, K., Chopra, K.: Genetic algorithm- a literature review. IEEE, India pp. 380\u2013384 (2019). https:\/\/ieeexplore.ieee.org\/abstract\/document\/8862255","DOI":"10.1109\/COMITCon.2019.8862255"},{"key":"4230_CR20","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1016\/j.simpat.2007.06.004","volume":"15","author":"A Chaibakhsh","year":"2007","unstructured":"Chaibakhsh, A., Ghaffari, A., Moosavian, S.A.A.: A simulated model for a once-through boiler by parameter adjustment based on genetic algorithms. Simulation Model. Practice Theory 15, 1029\u20131051 (2007)","journal-title":"Simulation Model. Practice Theory"},{"key":"4230_CR21","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.3390\/s24061842","volume":"24","author":"F Musialek","year":"2024","unstructured":"Musialek, F., Szabra, D., Wojtas, J.: Time-efficient snr optimization of WMS-based gas sensor using a genetic algorithm. Sensors 24, 1842 (2024)","journal-title":"Sensors"},{"key":"4230_CR22","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1080\/03052150802344477","volume":"40","author":"NB Hui","year":"2008","unstructured":"Hui, N.B., Pratihar, D.K.: Camera calibration using a genetic algorithm. Eng. Optim. 40, 1151\u20131169 (2008)","journal-title":"Eng. Optim."},{"key":"4230_CR23","doi-asserted-by":"crossref","unstructured":"Brenningmeyer, L.M., Popescu, P., Lei, P., Alvey, B.J., Anderson, D.T.: AugEvo: evolving augmentations to close the sim-to-real gap for AI | SPIE Defense + Commercial Sensing (2025). https:\/\/spie.org\/defense-commercial-sensing\/presentation\/AugEvo--evolving-augmentations-to-close-the-sim-to-real\/13459-7","DOI":"10.1117\/12.3052845"},{"key":"4230_CR24","doi-asserted-by":"publisher","first-page":"5099","DOI":"10.1080\/01431160500254999","volume":"26","author":"PM Atkinson","year":"2005","unstructured":"Atkinson, P.M., Sargent, I.M., Foody, G.M., Williams, J.: Interpreting image-based methods for estimating the signal-to-noise ratio. Int. J. Remote Sens. 26, 5099\u20135115 (2005)","journal-title":"Int. J. Remote Sens."},{"key":"4230_CR25","doi-asserted-by":"publisher","first-page":"11534","DOI":"10.1109\/ACCESS.2018.2796632","volume":"6","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., et al.: Can signal-to-noise ratio perform as a baseline indicator for medical image quality assessment. IEEE Access 6, 11534\u201311543 (2018)","journal-title":"IEEE Access"},{"key":"4230_CR26","unstructured":"Gilroy, S., O\u2019Dwyer, J., Bortoleto, L.: Characterisation of CMOS image sensor performance in low light automotive applications (2020). ArXiv:2011.12436 [cs, eess]"},{"key":"4230_CR27","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1364\/JOSA.38.000196","volume":"38","author":"A Rose","year":"1948","unstructured":"Rose, A.: The sensitivity performance of the human eye on an absolute scale*. J. Opt. Soc. America 38, 196\u2013208 (1948)","journal-title":"J. Opt. Soc. America"},{"key":"4230_CR28","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1109\/TIP.2007.901820","volume":"16","author":"DM Chandler","year":"2007","unstructured":"Chandler, D.M., Hemami, S.S.: VSNR: a wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans. Image Process. 16, 2284\u20132298 (2007)","journal-title":"IEEE Trans. Image Process."},{"key":"4230_CR29","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1364\/JOSAA.16.000633","volume":"16","author":"AE Burgess","year":"1999","unstructured":"Burgess, A.E.: The rose model, revisited. J. Opt. Soc. America 16, 633\u2013646 (1999)","journal-title":"J. Opt. Soc. America"},{"key":"4230_CR30","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TIP.2005.859378","volume":"15","author":"H Sheikh","year":"2006","unstructured":"Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Trans. Image Process. 15, 430\u2013444 (2006)","journal-title":"IEEE Trans. Image Process."},{"key":"4230_CR31","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.ins.2012.02.042","volume":"198","author":"S Mitra","year":"2012","unstructured":"Mitra, S., Kundu, P.P., Pedrycz, W.: Feature selection using structural similarity. Inf. Sci. 198, 48\u201361 (2012)","journal-title":"Inf. Sci."},{"key":"4230_CR32","doi-asserted-by":"crossref","unstructured":"Qin, X., et\u00a0al.: BASNet: boundary-aware salient object detection. CVF pp. 7479\u20137489 (2019). https:\/\/openaccess.thecvf.com\/content_CVPR_2019\/html\/Qin_BASNet_Boundary-Aware_Salient_Object_Detection_CVPR_2019_paper.html","DOI":"10.1109\/CVPR.2019.00766"},{"key":"4230_CR33","first-page":"1361","volume":"01","author":"G Chen","year":"2015","unstructured":"Chen, G., Shen, Y., Yao, F., Liu, P., Liu, Y.: Region-based moving object detection Using SSIM. IEEE 01, 1361\u20131364 (2015)","journal-title":"IEEE"},{"key":"4230_CR34","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: graphdeco-inria\/gaussian-splatting: original reference implementation of \"3D gaussian splatting for real-time radiance field rendering\". https:\/\/github.com\/graphdeco-inria\/gaussian-splatting"},{"key":"4230_CR35","unstructured":"Mardikes, M.: Synthetic-image-quality-analysis-calculator (2025). https:\/\/github.com\/mm18pu\/Synthetic-Image-Quality-Analysis-Calculator"},{"key":"4230_CR36","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). ArXiv:1512.03385 [cs]"},{"key":"4230_CR37","doi-asserted-by":"publisher","DOI":"10.6084\/m9.figshare.30170287","author":"M Mardikes","year":"2025","unstructured":"Mardikes, M.: Ga iqa data (2025). https:\/\/doi.org\/10.6084\/m9.figshare.30170287","journal-title":"Ga iqa data"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04230-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04230-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04230-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T12:45:25Z","timestamp":1772628325000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04230-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["4230"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04230-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.174802987.74303969\/v1","asserted-by":"object"}]},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"15 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"132"}}