{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:03:58Z","timestamp":1790071438292,"version":"4.0.1"},"reference-count":47,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T00:00:00Z","timestamp":1789862400000},"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>Image precompensation aimed at correcting refractive errors of the human eye seeks to transform a displayed image so that, after being blurred by the observer\u2019s ocular optics, the image projection on the retina closely matches the original picture. The optimal precompensation varies for different refractive errors, so taking into account the specific refractive distortion introduced by a particular eye is called personalization. Existing personalized precompensation methods rely on deconvolution with built-in constraints or further tone mapping, which either produce artifacts or impose contrast loss. In this paper, we propose an approach for adapting modern two-input neural networks developed for non-blind image deconvolution to the problem of personalized image precompensation. The purpose of two inputs is the capability of independently feeding the neural-network model with the image to process and the personal characteristic function of the observer\u2019s eye. Based on the proposed technique, three neural-network models (USRNet-PC, DWDN-PC, and KerUnc-PC) were developed, the adaptation of which was rather a unified approach than individual transformation of each architecture. These models were then comprehensively compared with other modern personalized precompensation methods on the basis of objective quality metrics, computational performance, and the results of simulation-based human studies. It has been shown that USRNet-PC provides the best subjective quality of perception and the shortest processing time on a GPU, while DWDN-PC demonstrates the highest computational efficiency on x86 and ARM CPUs. Thus, a new neural-network approach is proposed for solving the precompensation problem, which appeared to be superior in quality to previously known methods.<\/jats:p>","DOI":"10.3390\/jimaging12090456","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T08:17:54Z","timestamp":1790065074000},"page":"456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Input Neural Networks for Personalized Image Precompensation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0113-0865","authenticated-orcid":false,"given":"Nafe","family":"Alkzir","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, HSE University, Moscow 109028, Russia"},{"name":"Federal Research Center \u201cComputer Science and Control\u201d of the Russian Academy of Sciences, Moscow 119333, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5984-9995","authenticated-orcid":false,"given":"Maria","family":"Yarykina","sequence":"additional","affiliation":[{"name":"Federal Research Center \u201cComputer Science and Control\u201d of the Russian Academy of Sciences, Moscow 119333, Russia"},{"name":"Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow 127051, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0800-3140","authenticated-orcid":false,"given":"Alexander","family":"Abgaryan","sequence":"additional","affiliation":[{"name":"Federal Research Center \u201cComputer Science and Control\u201d of the Russian Academy of Sciences, Moscow 119333, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2425-207X","authenticated-orcid":false,"given":"Sergey","family":"Gladilin","sequence":"additional","affiliation":[{"name":"Federal Research Center \u201cComputer Science and Control\u201d of the Russian Academy of Sciences, Moscow 119333, Russia"},{"name":"Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow 127051, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5560-7668","authenticated-orcid":false,"given":"Dmitry","family":"Nikolaev","sequence":"additional","affiliation":[{"name":"Federal Research Center \u201cComputer Science and Control\u201d of the Russian Academy of Sciences, Moscow 119333, Russia"},{"name":"Smart Engines Service LLC, Moscow 117312, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1930-0799","authenticated-orcid":false,"given":"Ilya","family":"Nikolaev","sequence":"additional","affiliation":[{"name":"Institute for Information Transmission Problems of the Russian Academy of Sciences, Moscow 127051, Russia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1016\/j.ophtha.2016.01.006","article-title":"Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050","volume":"123","author":"Holden","year":"2016","journal-title":"Ophthalmology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.2147\/OPTO.S198637","article-title":"A review of contact lens dropout","volume":"12","author":"Pucker","year":"2020","journal-title":"Clin. Optom."},{"key":"ref_3","first-page":"42","article-title":"Overview of virtual and augmented reality technology","volume":"69","author":"Slavin","year":"2019","journal-title":"Tr. Instituta Sist. Anal. Ross. Akad. Nauk (ISA RAN)"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/3441290","article-title":"The state of virtual reality hardware","volume":"64","author":"Kugler","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1791","DOI":"10.1364\/AO.477296","article-title":"Dynamic correction of astigmatism","volume":"62","author":"Bhowmick","year":"2023","journal-title":"Appl. Opt."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2166","DOI":"10.1364\/BOE.485776","article-title":"ChromaCorrect: Prescription correction in virtual reality headsets through perceptual guidance","volume":"14","author":"Beyazian","year":"2023","journal-title":"Biomed. Opt. Express"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Thibos, L.N. (2000). Formation and sampling of the retinal image. Seeing, Elsevier.","DOI":"10.1016\/B978-012443760-9\/50003-9"},{"key":"ref_8","first-page":"4","article-title":"Results of studies of wave fronts using the first Russian-made aberrometer, Multispot-250","volume":"2","author":"Yablokov","year":"2005","journal-title":"Oftal\u2019mokhirurgiya"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1364\/AO.511482","article-title":"Aberration pre-correction for a simple optical system of HMDs","volume":"63","author":"Zhu","year":"2024","journal-title":"Appl. Opt."},{"key":"ref_10","unstructured":"Brown, M.S., Song, P., and Cham, T.J. (2006, January 17\u201322). Image pre-conditioning for out-of-focus projector blur. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), Piscataway, NJ, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1007\/s11055-024-01745-0","article-title":"Development of Image Preprocessing Methods for Software Compensation of Refraction Anomalies of an Observer\u2019s Eyes","volume":"54","author":"Alkzir","year":"2024","journal-title":"Neurosci. Behav. Physiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1117\/12.7972868","article-title":"Adaptive filtering for image enhancement","volume":"21","author":"Peli","year":"1982","journal-title":"Opt. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1117\/12.7973251","article-title":"Image enhancement for the visually impaired","volume":"23","author":"Peli","year":"1984","journal-title":"Opt. Eng."},{"key":"ref_14","first-page":"67","article-title":"Image contrast improvement method using genetic algorithm","volume":"2","author":"Gridin","year":"2023","journal-title":"J. Inf. Technol. Comput. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1109\/TIP.2022.3177129","article-title":"Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement","volume":"31","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2469","DOI":"10.1109\/TCSVT.2023.3299314","article-title":"Underwater image enhancement via weighted wavelet visual perception fusion","volume":"34","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","unstructured":"Alonso, M., and Barreto, A.B. (2003, January 17\u201321). Pre-compensation for high-order aberrations of the human eye using on-screen image deconvolution. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), Cancun, Mexico."},{"key":"ref_18","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Prentice Hall."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/01449290412331327456","article-title":"Image pre-compensation to facilitate computer access for users with refractive errors","volume":"24","author":"Alonso","year":"2005","journal-title":"Behav. Inf. Technol."},{"key":"ref_20","unstructured":"Alonso, M., Barreto, A., Jacko, J.A., Adjouadi, M., and Choudhury, M. (2005, January 8\u201310). Improving computer interaction for users with visual acuity deficiencies through inverse point spread function processing. Proceedings of the IEEE SoutheastCon, Ft. Lauderdale, FL, USA."},{"key":"ref_21","first-page":"179","article-title":"Contrast improvement in pre-compensation of ocular aberrations for computer users","volume":"48","author":"Huang","year":"2012","journal-title":"Biomed. Sci. Instrum."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alkzir, N.B., Nikolaev, I.P., and Nikolaev, D.P. (2023, January 20\u201323). SCA-2023: A Two-Part Dataset for Benchmarking the Methods of Image Precompensation for Users with Refractive Errors. Proceedings of the ECMS, Florence, Italy.","DOI":"10.7148\/2023-0298"},{"key":"ref_23","first-page":"1","article-title":"Correcting for optical aberrations using multilayer displays","volume":"31","author":"Huang","year":"2012","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ji, Y., Ye, J., Bing Kang, S., and Yu, J. (2014, January 23\u201328). Image pre-compensation: Balancing contrast and ringing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.428"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2717307","article-title":"A total variation approach for customizing imagery to improve visual acuity","volume":"34","author":"Montalto","year":"2015","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xu, F., and Li, D. (2018, January 18\u201322). Software based visual aberration correction for hmds. Proceedings of the 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Tuebingen\/Reutlingen, Germany.","DOI":"10.1109\/VR.2018.8447557"},{"key":"ref_27","unstructured":"Krishnan, D., and Fergus, R. (2009, January 7\u201310). Fast image deconvolution using hyper-Laplacian priors. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tanaka, H., and Kawano, H. (2021, January 19\u201322). Image correction for improving visual acuity using Zernike-based vision simulation. Proceedings of the 2021 20th International Symposium on Communications and Information Technologies (ISCIT), Tottori, Japan.","DOI":"10.1109\/ISCIT52804.2021.9590607"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huang, J., Barreto, A., Alonso, M., and Adjouadi, M. (September, January 30). Vision correction for computer users based on image pre-compensation with changing pupil size. Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.","DOI":"10.1109\/IEMBS.2011.6091206"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1109\/TPAMI.2018.2839115","article-title":"Content aware image pre-compensation","volume":"41","author":"Ye","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100654","DOI":"10.1016\/j.array.2025.100654","article-title":"Gaze-adaptive neural pre-correction for mitigating spatially varying optical aberrations in near-eye displays","volume":"29","author":"Jiang","year":"2026","journal-title":"Array"},{"key":"ref_32","unstructured":"Ridnik, T., Ben-Baruch, E., Noy, A., and Zelnik-Manor, L. (2021). Imagenet-21k pretraining for the masses. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1109\/TIP.2009.2028250","article-title":"Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems","volume":"18","author":"Beck","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/BF02592073","article-title":"Projected gradient methods for linearly constrained problems","volume":"39","author":"Calamai","year":"1987","journal-title":"Math. Program."},{"key":"ref_36","unstructured":"Alkzir, N.B., Terekhin, A.P., Nikolaev, D.P., Gladilin, S.A., and Konovalenko, I.A. (2025, May 06). PyOLimp: A Framework for Image Precompensation and Optimization. Available online: https:\/\/github.com\/pyolimp\/pyolimp."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"562","DOI":"10.18287\/2412-6179-CO-1409","article-title":"Applied aspects of modern non-blind image deconvolution methods","volume":"48","author":"Chaganova","year":"2024","journal-title":"Comput. Opt."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, K., Gool, L.V., and Timofte, R. (2020, January 14\u201319). Deep unfolding network for image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR42600.2020.00328"},{"key":"ref_39","unstructured":"Dong, J., Roth, S., and Schiele, B. (2020, January 6\u201312). Deep Wiener deconvolution: Wiener meets deep learning for image deblurring. Proceedings of the Advances in Neural Information Processing Systems, Virtual."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nan, Y., and Ji, H. (2020, January 14\u201319). Deep learning for handling kernel\/model uncertainty in image deconvolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR42600.2020.00246"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alkzir, N.B., Nikolaev, I.P., and Nikolaev, D.P. (2023, January 15\u201318). Search for image quality metrics suitable for assessing images specially precompensated for users with refractive errors. Proceedings of the Sixteenth International Conference on Machine Vision (ICMV 2023), Yerevan, Armenia.","DOI":"10.1117\/12.3023509"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201322). The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3758\/BF03206343","article-title":"Two-alternative versus three-alternative procedures for threshold estimation","volume":"35","author":"Shelton","year":"1984","journal-title":"Percept. Psychophys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1111\/j.1467-8659.2012.03188.x","article-title":"Comparison of four subjective methods for image quality assessment","volume":"31","author":"Mantiuk","year":"2012","journal-title":"Comput. Graph. Forum"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"McCullagh, P., Nelder, J.A., and McCullagh, P. (1989). Generalized Linear Models, Chapman and Hall.","DOI":"10.1007\/978-1-4899-3242-6"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1198\/106186006X96962","article-title":"Efficient Laplacian and adaptive Gaussian quadrature algorithms for multilevel generalized linear mixed models","volume":"15","author":"Pinheiro","year":"2006","journal-title":"J. Comput. Graph. Stat."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/456\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:21:31Z","timestamp":1790068891000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,20]]},"references-count":47,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jimaging12090456"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12090456","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,20]]}}}