{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:22:59Z","timestamp":1760145779764,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Centre of Science (Poland)","award":["2021\/43\/B\/ST6\/02853","PLG\/2023\/016646"],"award-info":[{"award-number":["2021\/43\/B\/ST6\/02853","PLG\/2023\/016646"]}]},{"name":"Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH, CI TASK)","award":["2021\/43\/B\/ST6\/02853","PLG\/2023\/016646"],"award-info":[{"award-number":["2021\/43\/B\/ST6\/02853","PLG\/2023\/016646"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Enhancing low-light images with natural colors poses a challenge due to camera processing variations and limited access to ground-truth lighting conditions. To address this, we propose Dimma, a semi-supervised approach that aligns with any camera using a small set of image pairs captured under extreme lighting conditions. Our method employs a convolutional mixture density network to replicate camera-specific noise present in dark images. We enhance results further by introducing a conditional UNet architecture based on user-provided lightness values. Trained on just a few real image pairs, Dimma achieves competitive results compared to fully supervised state-of-the-art methods trained on large datasets.<\/jats:p>","DOI":"10.3390\/e26090726","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T03:51:06Z","timestamp":1724730666000},"page":"726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dimma: Semi-Supervised Low-Light Image Enhancement with Adaptive Dimming"],"prefix":"10.3390","volume":"26","author":[{"given":"Wojciech","family":"Koz\u0142owski","sequence":"first","affiliation":[{"name":"Faculty of Information and Communication Technology, Wroc\u0142aw University of Science and Technology, 50-370 Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Micha\u0142","family":"Szachniewicz","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Wroc\u0142aw University of Science and Technology, 50-370 Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Micha\u0142","family":"Stypu\u0142kowski","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, University of Wroc\u0142aw, 50-384 Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4217-7712","authenticated-orcid":false,"given":"Maciej","family":"Zi\u0119ba","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Technology, Wroc\u0142aw University of Science and Technology, 50-370 Wroc\u0142aw, Poland"},{"name":"Tooploox Ltd., 53-601 Wroc\u0142aw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"ref_1","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep retinex decomposition for low-light enhancement. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., and Guo, X. (2019, January 21\u201325). Kindling the darkness: A practical low-light image enhancer. Proceedings of the 27th ACM international Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3350926"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s11263-020-01407-x","article-title":"Beyond brightening low-light images","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_4","unstructured":"Wang, Y., Wan, R., Yang, W., Li, H., Chau, L.P., and Kot, A. (March, January 22). Low-light image enhancement with normalizing flow. Proceedings of the AAAI Conference on Artificial Intelligence, Online."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cai, Y., Bian, H., Lin, J., Wang, H., Timofte, R., and Zhang, Y. (2023). Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement. arXiv.","DOI":"10.1109\/ICCV51070.2023.01149"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., and Wang, Z. (2021). EnlightenGAN: Deep Light Enhancement without Paired Supervision. arXiv.","DOI":"10.1109\/TIP.2021.3051462"},{"key":"ref_7","unstructured":"Zhang, F., Shao, Y., Sun, Y., Zhu, K., Gao, C., and Sang, N. (2021). Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the 18th international conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_9","unstructured":"Bishop, C.M. (1994). Mixture Density Networks, Aston University."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_12","unstructured":"Toderici, G., Shi, W., Timofte, R., Theis, L., Ball\u00e9, J., Agustsson, E., Johnston, N., and Mentzer, F. (2020, January 13\u201319). Workshop and challenge on learned image compression (clic2020). Proceedings of the CVPR, Seattle, WA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/TIP.2022.3227503","article-title":"Adapool: Exponential adaptive pooling for information-retaining downsampling","volume":"32","author":"Stergiou","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guo, X. (2016, January 15\u201319). Lime: A method for low-light image enhancement. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967188"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhou, D., Yang, Z., and Yang, Y. (2023). Pyramid Diffusion Models For Low-light Image Enhancement. arXiv.","DOI":"10.24963\/ijcai.2023\/199"},{"key":"ref_16","unstructured":"Hou, J., Zhu, Z., Hou, J., Liu, H., Zeng, H., and Yuan, H. (2023). Global structure-aware diffusion process for low-light image enhancement. arXiv."},{"key":"ref_17","first-page":"1","article-title":"Low-light image enhancement with wavelet-based diffusion models","volume":"42","author":"Jiang","year":"2023","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, X., Wang, R., Fu, C.W., and Jia, J. (2022, January 18\u201324). SNR-aware low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01719"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, H., Xu, X., Xu, K., and Lau, R.W. (2023). Lighting up NeRF via Unsupervised Decomposition and Enhancement. arXiv.","DOI":"10.1109\/ICCV51070.2023.01161"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 13\u201319). Zero-reference deep curve estimation for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jin, X., Xiao, J.W., Han, L.H., Guo, C., Zhang, R., Liu, X., and Li, C. (2023, January 1\u20136). Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01221"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, W., Wang, S., Fang, Y., Wang, Y., and Liu, J. (2020, January 13\u201319). From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00313"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., and Koltun, V. (2018, January 18\u201323). Learning to see in the dark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00347"},{"key":"ref_24","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","unstructured":"Nichol, A.Q., and Dhariwal, P. (2021, January 18\u201324). Improved denoising diffusion probabilistic models. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_26","unstructured":"von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T. (2024, June 21). Diffusers: State-of-the-Art Diffusion Models. Available online: https:\/\/github.com\/huggingface\/diffusers."},{"key":"ref_27","first-page":"10215","article-title":"Glow: Generative flow with invertible 1x1 convolutions","volume":"31","author":"Kingma","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","unstructured":"Johnson, J., Alahi, A., and Li, F.-F. (2016, January 11\u201314). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part II 14."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). 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_31","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201ccompletely blind\u201d image quality analyzer","volume":"20","author":"Mittal","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1007\/s11263-020-01418-8","article-title":"Benchmarking low-light image enhancement and beyond","volume":"129","author":"Liu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9396","DOI":"10.1109\/TPAMI.2021.3126387","article-title":"Low-light image and video enhancement using deep learning: A survey","volume":"44","author":"Li","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TIP.2018.2794218","article-title":"Learning a deep single image contrast enhancer from multi-exposure images","volume":"27","author":"Cai","year":"2018","journal-title":"IEEE Trans. Image Process."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/9\/726\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:43:08Z","timestamp":1760110988000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/9\/726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,26]]},"references-count":34,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["e26090726"],"URL":"https:\/\/doi.org\/10.3390\/e26090726","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2024,8,26]]}}}