{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T11:07:59Z","timestamp":1780657679680,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T00:00:00Z","timestamp":1663027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"Korean government (MIST)","doi-asserted-by":"publisher","award":["2020R1A2C1003897"],"award-info":[{"award-number":["2020R1A2C1003897"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study introduces a low-light image enhancement method using a hybrid deep-learning network and mixed-norm loss functions, in which the network consists of a decomposition-net, illuminance enhance-net, and chroma-net. To consider the correlation between R, G, and B channels, YCbCr channels converted from the RGB channels are used for training and restoration processes. With the luminance, the decomposition-net aims to decouple the reflectance and illuminance and to train the reflectance, leading to a more accurate feature map with noise reduction. The illumination enhance-net connected to the decomposition-net is used to enhance the illumination such that the illuminance is improved with reduced halo artifacts. In addition, the chroma-net is independently used to reduce color distortion. Moreover, a mixed-norm loss function used in the training process of each network is described to increase the stability and remove blurring in the reconstructed image by reflecting the properties of reflectance, illuminance, and chroma. The experimental results demonstrate that the proposed method leads to promising subjective and objective improvements over state-of-the-art deep-learning methods.<\/jats:p>","DOI":"10.3390\/s22186904","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T22:37:28Z","timestamp":1663108648000},"page":"6904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions"],"prefix":"10.3390","volume":"22","author":[{"given":"JongGeun","family":"Oh","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Soongsil University, Seoul 156-743, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min-Cheol","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Soongsil University, Seoul 156-743, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chien, J.-C., Chen, Y.-S., and Lee, J.-D. (2017). Improving night time driving safety using vision-based classification techniques. Sensors, 17.","DOI":"10.3390\/s17102199"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87884","DOI":"10.1109\/ACCESS.2020.2992749","article-title":"An experimental-based review of low-light image enhancement methods","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1364\/JOSA.61.000001","article-title":"Lightness and retinex theory","volume":"61","author":"Land","year":"1971","journal-title":"J. Opt. Soc. Am."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/83.557356","article-title":"Properties and performance of a center\/surround retinex","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rahman, Z., Jobson, D., and Woodell, G. (1996, January 16\u201319). Multi-scale retinex for color image enhancement. Proceedings of the 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland.","DOI":"10.1109\/ICIP.1996.560995"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/83.597272","article-title":"A multiscale retinex for bridging the gap between color images and the human observation of scenes","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TIP.2006.884946","article-title":"Random spray retinex: A new retinex implementation to investigate the local properties of the model","volume":"16","author":"Provenzi","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/LSP.2013.2285960","article-title":"Light random spray retinex: Exploiting the noisy illumination estimation","volume":"20","author":"Banic","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5209","DOI":"10.1109\/TIP.2014.2364537","article-title":"Spatial Entropy-Based Global and Local Image Contrast Enhancement","volume":"23","author":"Celik","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1049\/iet-ipr.2014.0437","article-title":"Efficient naturalness restoration for non-uniform illuminance images","volume":"9","author":"Shin","year":"2015","journal-title":"IET Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2767","DOI":"10.1109\/TIP.2017.2686652","article-title":"GRASS: A gradient-based random sampling scheme for Milano retinex","volume":"26","author":"Lecca","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"013006","DOI":"10.1117\/1.JEI.23.1.013006","article-title":"Termite retinex: A new implementation based on a colony of intelligent agents","volume":"23","author":"Simone","year":"2014","journal-title":"J. Electron. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dou, Z., Gao, K., Zhang, B., Yu, X., Han, L., and Zhu, Z. (2017). Realistic image rendition using a variable exponent functional model for retinex. Sensors, 16.","DOI":"10.3390\/s16060832"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1022314423998","article-title":"A variational framework for retinex","volume":"52","author":"Kimmel","year":"2003","journal-title":"Int. J. Comput. Vis."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1137\/140972664","article-title":"Non-local retinex-A unifying framework and beyond","volume":"8","author":"Zosso","year":"2015","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TCE.2017.014847","article-title":"Low-light image enhancement using variational optimization-based retinex model","volume":"63","author":"Park","year":"2017","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","article-title":"LLNet: A deep autoencoder approach to natural low-light image enhancement","volume":"61","author":"Lore","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_18","unstructured":"Shen, L., Yue, Z., Feng, F., Chen, Q., Liu, S., and Ma, J. (2017). MSR-net: Low-light image enhancement using deep convolutional network. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"Lime: Low-light image enhancement via illuminance map estimation","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep retinex decomposition for low-light enhancement. arXiv."},{"key":"ref_21","unstructured":"Lv, F., Lu, F., Wu, J., and Lim, C. (2018, January 3\u20136). MBLLEN: Low-light image\/video enhancement using CNNs. Proceedings of the British Machine Vision Conference (BMVC), Newcastle, UK."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, J., and Guo, X. (2019, January 15). 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_23","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"EnlightenGAN: Deep light enhancement without paired supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, B., Lee, S., Kim, N., Jang, D., and Kim, D.-S. (2022, January 3\u20138). Learning color representation for low-light image enhancement. Proceedings of the 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00098"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Oh, J.-G., and Hong, M.-C. (2019). Adaptive image rendering using a nonlinear mapping-function-based retinex model. Sensors, 19.","DOI":"10.3390\/s19040969"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kinoshita, Y., and Kiya, H. (2019, January 22\u201325). Convolutional neural networks considering local and global features for image enhancement. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803194"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Shahbaz, F., Yang, M.-H., and Shao, L. (2020, January 23\u201328). Learning enriched features for real image restoration and enhancement. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58595-2_30"},{"key":"ref_29","unstructured":"Anwar, S., Barnes, N., and Petersson, L. (2021). Attention-based real image restoration. IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","unstructured":"Kingman, D.P., and Ba, J. (2017). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_32","unstructured":"Sheikh, H.R., Wang, Z., Cormack, L., and Bovik, A.C. (2022, March 23). Live Image Quality Assessment Database Release 2. The Univ. of Texas at Austin. Available online: https:\/\/live.ece.utexas.edu\/research\/Quality\/subjective.htm."},{"key":"ref_33","unstructured":"Stanford Vision Lab (2022, May 18). ImageNet. Available online: http:\/\/image-net.org."},{"key":"ref_34","unstructured":"(2021, November 17). NASA Langley Research Center, Available online: https:\/\/dragon.larc.nasa.gov."},{"key":"ref_35","unstructured":"Arbelaez, P., Fowlkes, C., and Martin, D. (2022, February 07). The Berkeley Segmentation Dataset and Benchmark. Available online: https:\/\/www2.eecs.berkeley.edu\/Research\/Projects\/CS\/vision\/bsds\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1109\/TIP.2013.2261309","article-title":"Naturalness preserved enhancement algorithm for non-uniform illumination images","volume":"22","author":"Wang","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIP.2005.859378","article-title":"Image information and visual quality","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","unstructured":"Venkatanath, N., Praneeth, D., Chandrasekhar, B.H., Channappayya, S.S., and Medasani, S.S. (March, January 27). Blind image quality evaluation using perception based features. Proceedings of the 21st National Conference on Communications (NCC), Mumbai, India."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1364\/JOSAA.7.002032","article-title":"Contrast in complex images","volume":"7","author":"Peli","year":"1990","journal-title":"J. Opt. Soc. Am. 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