{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:07:20Z","timestamp":1760144840943,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T00:00:00Z","timestamp":1716768000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Basic Research Plan in Shannxi Province of China","award":["2023-JQ-QC-0714"],"award-info":[{"award-number":["2023-JQ-QC-0714"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The enhancement of images captured under low-light conditions plays a vitally important role in the area of image processing and can significantly affect the performance of following operations. In recent years, deep learning techniques have been leveraged in the area of low-light image enhancement tasks, and deep-learning-based low-light image enhancement methods have been the mainstream for low-light image enhancement tasks. However, due to the inability of existing methods to effectively maintain the color distribution of the original input image and to effectively handle feature descriptions at different scales, the final enhanced image exhibits color distortion and local blurring phenomena. So, in this paper, a novel dual color-and-texture-enhancement-based low-light image enhancement method is proposed, which can effectively enhance low-light images. Firstly, a novel color enhancement block is leveraged to help maintain color distribution during the enhancement process, which can further eliminate the color distortion effect; after that, an attention-based multiscale texture enhancement block is proposed to help the network focus on multiscale local regions and extract more reliable texture representations automatically, and a fusion strategy is leveraged to fuse the multiscale feature representations automatically and finally generate the enhanced reflection component. The experimental results on public datasets and real-world low-light images established the effectiveness of the proposed method on low-light image enhancement tasks.<\/jats:p>","DOI":"10.3390\/computers13060134","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:36:22Z","timestamp":1716798982000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DCTE-LLIE: A Dual Color-and-Texture-Enhancement-Based Method for Low-Light Image Enhancement"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2525-5479","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"first","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jianzhong","family":"Cao","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jijiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/TCE.2007.4429280","article-title":"Brightness preserving dynamic histogram equalization for image contrast enhancement","volume":"53","author":"Ibrahim","year":"2007","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nakai, K., Hoshi, Y., and Taguchi, A. (2013, January 12\u201315). Color image contrast enhacement method based on differential intensity\/saturation gray-levels histograms. Proceedings of the 2013 International Symposium on Intelligent Signal Processing and Communication Systems, Naha, Japan.","DOI":"10.1109\/ISPACS.2013.6704591"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3431","DOI":"10.1109\/TIP.2011.2157513","article-title":"Contextual and variational contrast enhancement","volume":"20","author":"Celik","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5372","DOI":"10.1109\/TIP.2013.2284059","article-title":"Contrast enhancement based on layered difference representation of 2D histograms","volume":"22","author":"Lee","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/scientificamerican1277-108","article-title":"The retinex theory of color vision","volume":"237","author":"Land","year":"1974","journal-title":"Sci. Am."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_9","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/TIP.2023.3243853","article-title":"Single-source domain expansion network for cross-scene hyperspectral image classification","volume":"32","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"120496","DOI":"10.1016\/j.eswa.2023.120496","article-title":"MFFCG\u2013Multi feature fusion for hyperspectral image classification using graph attention network","volume":"229","author":"Bhatti","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_13","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems 28, Montreal, QC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","article-title":"Object detection in 20 years: A survey","volume":"111","author":"Zou","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, S., Sun, P., Song, Y., and Luo, P. (2023, January 2\u20136). Diffusiondet: Diffusion model for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01816"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Y., Chen, X., Lim, S.N., Torralba, A., Zhao, H., and Wang, S. (2023, January 17\u201324). Detecting everything in the open world: Towards universal object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01100"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jain, J., Li, J., Chiu, M.T., Hassani, A., Orlov, N., and Shi, H. (2023, January 17\u201324). Oneformer: One transformer to rule universal image segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00292"},{"key":"ref_20","unstructured":"Wu, J., Fu, R., Fang, H., Zhang, Y., Yang, Y., Xiong, H., Liu, H., and Xu, Y. (2023, January 10\u201312). Medsegdiff: Medical image segmentation with diffusion probabilistic model. Proceedings of the Medical Imaging with Deep Learning, Nashville, NJ, USA."},{"key":"ref_21","unstructured":"Wei, C., Wang, W., Yang, W., and Liu, J. (2018). Deep retinex decomposition for low-light enhancement. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7984","DOI":"10.1109\/TIP.2020.3008396","article-title":"Lightening network for low-light image enhancement","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","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_24","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, Vancouver, BC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103712","DOI":"10.1016\/j.jvcir.2022.103712","article-title":"R2rnet: Low-light image enhancement via real-low to real-normal network","volume":"90","author":"Hai","year":"2023","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/TCI.2023.3240087","article-title":"LightingNet: An integrated learning method for low-light image enhancement","volume":"9","author":"Yang","year":"2023","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1007\/s11263-022-01667-9","article-title":"Low-light image enhancement via breaking down the darkness","volume":"131","author":"Guo","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive histogram equalization and its variations","volume":"39","author":"Pizer","year":"1987","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hussain, K., Rahman, S., Khaled, S.M., Abdullah-Al-Wadud, M., and Shoyaib, M. (2014, January 18\u201320). Dark image enhancement by locally transformed histogram. Proceedings of the 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014), Dhaka, Bangladesh.","DOI":"10.1109\/SKIMA.2014.7083541"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TCE.2011.5955195","article-title":"Contrast enhancement using non-overlapped sub-blocks and local histogram projection","volume":"57","author":"Liu","year":"2011","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/76.915354","article-title":"An advanced contrast enhancement using partially overlapped sub-block histogram equalization","volume":"11","author":"Kim","year":"2001","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Dong, X., Pang, Y., and Wen, J. (2010, January 26\u201330). Fast efficient algorithm for enhancement of low lighting video. Proceedings of the ACM SIGGRAPH 2010 Posters, Los Angeles, CA, USA.","DOI":"10.1145\/1836845.1836920"},{"key":"ref_34","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_35","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_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":"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_38","doi-asserted-by":"crossref","unstructured":"Li, M., Liu, J., Yang, W., and Guo, Z. (2017, January 8\u20139). Joint denoising and enhancement for low-light images via retinex model. Proceedings of the International Forum on Digital TV and Wireless Multimedia Communications, Shanghai, China.","DOI":"10.1007\/978-981-10-8108-8_9"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/TIP.2018.2810539","article-title":"Structure-revealing low-light image enhancement via robust retinex model","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems 30, Long Beach, CA, USA."},{"key":"ref_41","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_42","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 Conerence on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.patrec.2018.01.010","article-title":"LightenNet: A convolutional neural network for weakly illuminated image enhancement","volume":"104","author":"Li","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_45","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, Newcastle, UK."},{"key":"ref_46","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_47","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_48","doi-asserted-by":"crossref","first-page":"4093","DOI":"10.1109\/TMM.2020.3037526","article-title":"TBEFN: A two-branch exposure-fusion network for low-light image enhancement","volume":"23","author":"Lu","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_49","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_50","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_51","doi-asserted-by":"crossref","unstructured":"Yang, W., Wang, S., Fang, Y., Wang, Y., and Liu, J. (2020, January 14\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_52","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhang, L., Liu, X., Shen, Y., Zhang, S., and Zhao, S. (2019, January 21\u201325). Zero-shot restoration of back-lit images using deep internal learning. Proceedings of the 27th ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3351069"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 14\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_54","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"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/6\/134\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:51Z","timestamp":1760107731000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/13\/6\/134"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,27]]},"references-count":54,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["computers13060134"],"URL":"https:\/\/doi.org\/10.3390\/computers13060134","relation":{},"ISSN":["2073-431X"],"issn-type":[{"type":"electronic","value":"2073-431X"}],"subject":[],"published":{"date-parts":[[2024,5,27]]}}}