{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:33:34Z","timestamp":1760060014613,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"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>Imaging technologies are widely used in surveillance, medical diagnostics, and other critical applications. However, under low-light conditions, captured images often suffer from insufficient brightness, blurred details, and excessive noise, degrading quality and hindering downstream tasks. Conventional low-light image enhancement (LLIE) methods not only require annotated data but also often involve heavy models with high computational costs, making them unsuitable for real-time processing. To tackle these challenges, a lightweight and unsupervised LLIE method utilizing a dual-stage frequency-domain calibration network (DFCNet) is proposed. In the first stage, the input image undergoes the preliminary feature modulation (PFM) module to guide the illumination estimation (IE) module in generating a more accurate illumination map. The final enhanced image is obtained by dividing the input by the estimated illumination map. The second stage is used only during training. It applies a frequency-domain residual calibration (FRC) module to the first-stage output, generating a calibration term that is added to the original input to darken dark regions and brighten bright areas. This updated input is then fed back to the PFM and IE modules for parameter optimization. Extensive experiments on benchmark datasets demonstrate that DFCNet achieves superior performance across multiple image quality metrics while delivering visually clearer and more natural results.<\/jats:p>","DOI":"10.3390\/jimaging11080253","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T08:51:33Z","timestamp":1753692693000},"page":"253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DFCNet: Dual-Stage Frequency-Domain Calibration Network for Low-Light Image Enhancement"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2775-3026","authenticated-orcid":false,"given":"Hui","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2457-9032","authenticated-orcid":false,"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Yaming","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0556-1470","authenticated-orcid":false,"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2107-7858","authenticated-orcid":false,"given":"Yiyang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1109\/TPAMI.2023.3330416","article-title":"Image Restoration via Frequency Selection","volume":"46","author":"Cui","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","first-page":"4","article-title":"Attention-Based Deep Learning Framework for Action Recognition in a Dark Environment","volume":"14","author":"Munsif","year":"2024","journal-title":"Hum. Centric Comput. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Herath, H., Herath, H., Madusanka, N., and Lee, B.-I. (2025). A Systematic Review of Medical Image Quality Assessment. J. Imaging, 11.","DOI":"10.3390\/jimaging11040100"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cheng, B., Zhang, T., Zhao, Y., Fu, T., Wu, Z., and Tao, X. (2024). MIMO-Uformer: A Transformer-Based Image Deblurring Network for Vehicle Surveillance Scenarios. J. Imaging, 10.","DOI":"10.3390\/jimaging10110274"},{"key":"ref_5","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep Retinex Decomposition for Low-Light Enhancement. arXiv."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Tatana, M.M., Tsoeu, M.S., and Maswanganyi, R.C. (2025). Low-Light Image and Video Enhancement for More Robust Computer Vision Tasks: A Review. J. Imaging, 11.","DOI":"10.3390\/jimaging11040125"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e14558","DOI":"10.1016\/j.heliyon.2023.e14558","article-title":"A Survey on Image Enhancement for Low-Light Images","volume":"9","author":"Guo","year":"2023","journal-title":"Heliyon"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Li, D., Zhang, Y., Wen, P., and Bai, L. (2015, January 19\u201320). A Retinex Algorithm for Image Enhancement Based on Recursive Bilateral Filtering. Proceedings of the 2015 11th International Conference on Computational Intelligence and Security (CIS), Shenzhen, China.","DOI":"10.1109\/CIS.2015.45"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-Light Image Enhancement via Illumination Map Estimation","volume":"26","author":"Guo","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_12","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_13","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 Bmvc, Newcastle, UK."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.1109\/TMM.2020.3039361","article-title":"DSLR: Deep Stacked Laplacian Restorer for Low-Light Image Enhancement","volume":"23","author":"Lim","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., and Jiang, J. (2022, January 18\u201324). Uretinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00581"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8436","DOI":"10.1109\/TCSVT.2022.3194169","article-title":"Light-Guided and Cross-Fusion U-Net for Anti-Illumination Image Super-Resolution","volume":"32","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, H., Xu, K., and Lau, R.W. (2022, January 23\u201327). Local Color Distributions Prior for Image Enhancement. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19797-0_20"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cai, Y., Bian, H., Lin, J., Wang, H., Timofte, R., and Zhang, Y. (2023, January 2\u20136). Retinexformer: One-Stage Retinex-Based Transformer for Low-Light Image Enhancement. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01149"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bai, J., Yin, Y., He, Q., Li, Y., and Zhang, X. (2024). Retinexmamba: Retinex-Based Mamba for Low-Light Image Enhancement. arXiv.","DOI":"10.1007\/978-981-96-6596-9_30"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4706516","DOI":"10.1109\/TGRS.2024.3434416","article-title":"Spatial-Frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement","volume":"62","author":"Yao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, W., Wei, C., Yang, W., and Liu, J. (2018, January 15\u201319). Gladnet: Low-Light Enhancement Network with Global Awareness. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00118"},{"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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, A., Zhang, L., Shen, Y., Ma, Y., Zhao, S., and Zhou, Y. (2020, January 6\u201310). Zero-Shot Restoration of Underexposed Images via Robust Retinex Decomposition. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102962"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1109\/TIP.2021.3062184","article-title":"Band Representation-Based Semi-Supervised Low-Light Image Enhancement: Bridging the Gap Between Signal Fidelity and Perceptual Quality","volume":"30","author":"Yang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TCSVT.2021.3073371","article-title":"RetinexDIP: A Unified Deep Framework for Low-Light Image Enhancement","volume":"32","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110","DOI":"10.26599\/BDMA.2021.9020020","article-title":"MAGAN: Unsupervised Low-Light Image Enhancement Guided by Mixed-Attention","volume":"5","author":"Wang","year":"2022","journal-title":"Big Data Min. Anal."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ma, L., Ma, T., Liu, R., Fan, X., and Luo, Z. (2022, January 18\u201324). Toward Fast, Flexible, and Robust Low-Light Image Enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00555"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3726","DOI":"10.1109\/TCSVT.2023.3241162","article-title":"Illumination-Adaptive Unpaired Low-Light Enhancement","volume":"33","author":"Kandula","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liang, Z., Li, C., Zhou, S., Feng, R., and Loy, C.C. (2023, January 1\u20136). Iterative Prompt Learning for Unsupervised Backlit Image Enhancement. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00743"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"19440","DOI":"10.1109\/TITS.2022.3165176","article-title":"Unsupervised Decomposition and Correction Network for Low-Light Image Enhancement","volume":"23","author":"Jiang","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fei, B., Lyu, Z., Pan, L., Zhang, J., Yang, W., Luo, T., Zhang, B., and Dai, B. (2023, January 17\u201324). Generative Diffusion Prior for Unified Image Restoration and Enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00958"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5951","DOI":"10.1109\/TPAMI.2024.3382108","article-title":"Unsupervised Illumination Adaptation for Low-Light Vision","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3029","DOI":"10.1109\/TETCI.2024.3369858","article-title":"Unsupervised Low-Light Image Enhancement via Luminance Mask and Luminance-Independent Representation Decoupling","volume":"8","author":"Peng","year":"2024","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_36","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 Medical Image Computing and Computer-Assisted Intervention\u2013Miccai 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, L., Dong, J., Tang, J., and Pan, J. (2023, January 2\u20133). Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01213"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rowlands, D.A., and Finlayson, G.D. (2024). Optimisation of Convolution-Based Image Lightness Processing. J. Imaging, 10.","DOI":"10.3390\/jimaging10080204"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., and Jia, J. (2019, January 15\u201320). Underexposed Photo Enhancement Using Deep Illumination Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00701"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_41","unstructured":"Zhu, X., Cheng, D., Zhang, Z., Lin, S., and Dai, J. (November, January 27). An Empirical Study of Spatial Attention Mechanisms in Deep Networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_42","unstructured":"Li, C., Guo, C.-L., Zhou, M., Liang, Z., Zhou, S., Feng, R., and Loy, C.C. (2023). Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement. arXiv."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"2072","DOI":"10.1109\/TIP.2021.3050850","article-title":"Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement","volume":"30","author":"Yang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-Reference Image Quality Assessment in the Spatial Domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bychkovsky, V., Paris, S., Chan, E., and Durand, F. (2011, January 20\u201325). Learning Photographic Global Tonal Adjustment with a Database of Input\/Output Image Pairs. Proceedings of the Cvpr 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995332"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.cviu.2018.10.010","article-title":"Getting to Know Low-Light Images with the Exclusively Dark Dataset","volume":"178","author":"Loh","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5737","DOI":"10.1109\/TIP.2020.2981922","article-title":"Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study","volume":"29","author":"Yang","year":"2020","journal-title":"IEEE Trans. Image Process."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:17:06Z","timestamp":1760033826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,28]]},"references-count":49,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jimaging11080253"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11080253","relation":{},"ISSN":["2313-433X"],"issn-type":[{"type":"electronic","value":"2313-433X"}],"subject":[],"published":{"date-parts":[[2025,7,28]]}}}