{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:07:18Z","timestamp":1774030038091,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s11760-023-02797-4","type":"journal-article","created":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T17:02:09Z","timestamp":1698339729000},"page":"1089-1097","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["MMFF-NET: Multi-layer and multi-scale feature fusion network for low-light infrared image enhancement"],"prefix":"10.1007","volume":"18","author":[{"given":"Ge","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Yuhan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xianquan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yiheng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,26]]},"reference":[{"issue":"5","key":"2797_CR1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.31.5.051408","volume":"31","author":"X Li","year":"2022","unstructured":"Li, X.: Infrared image filtering and enhancement processing method based upon image processing technology. J. Electron. Imag. 31(5), 051408 (2022). https:\/\/doi.org\/10.1117\/1.JEI.31.5.051408","journal-title":"J. Electron. Imag."},{"issue":"4","key":"2797_CR2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.31.4.043023","volume":"31","author":"X Gao","year":"2022","unstructured":"Gao, X., Liu, S.: DAFuse: a fusion for infrared and visible images based on generative adversarial network. J. Electron. Imag. 31(4), 043023 (2022). https:\/\/doi.org\/10.1117\/1.JEI.31.4.043023","journal-title":"J. Electron. Imag."},{"issue":"3","key":"2797_CR3","doi-asserted-by":"publisher","DOI":"10.1117\/1.JEI.31.3.033029","volume":"31","author":"G Yue","year":"2022","unstructured":"Yue, G., Li, Z., Tao, Y., Jin, T.: Low-illumination traffic object detection using the saliency region of infrared image masking on infrared-visible fusion image. J. Electron. Imag. 31(3), 033029 (2022). https:\/\/doi.org\/10.1117\/1.JEI.31.3.033029","journal-title":"J. Electron. Imag."},{"key":"2797_CR4","doi-asserted-by":"publisher","unstructured":"Ye, Y.X., Shen, L.: HOPC: A novel similarity metric based on geometric structural properties for multi-modal remote sensing image matching. In: Proceedings of ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 9\u201316 (2016). https:\/\/doi.org\/10.48550\/arXiv.1408.3809","DOI":"10.48550\/arXiv.1408.3809"},{"key":"2797_CR5","doi-asserted-by":"publisher","unstructured":"Fu, X., Zeng, D., Huang, Y., et al.: A weighted variational model for simultaneous reflectance and illumination estimation. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 2782\u20132790 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.304","DOI":"10.1109\/CVPR.2016.304"},{"key":"2797_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2019.163300","volume":"199","author":"Y Li","year":"2019","unstructured":"Li, Y., Liu, N., Xu, J., Wu, J.: Detail enhancement of infrared image based on bi-exponential edge preserving smoother. Optik 199, 163300 (2019). https:\/\/doi.org\/10.1016\/j.ijleo.2019.163300","journal-title":"Optik"},{"key":"2797_CR7","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.infrared.2018.03.010","volume":"90","author":"S Li","year":"2018","unstructured":"Li, S., Jin, W., Li, L., Li, Y.: An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization. Infrared Phys. Technol. 90, 164\u2013174 (2018). https:\/\/doi.org\/10.1016\/j.infrared.2018.03.010","journal-title":"Infrared Phys. Technol."},{"key":"2797_CR8","doi-asserted-by":"publisher","first-page":"4039","DOI":"10.1016\/j.ijleo.2014.01.117","volume":"125","author":"J Zhao","year":"2014","unstructured":"Zhao, J., Chen, Y., Feng, H., Xu, Z., Li, Q.: Fast image enhancement using multi-scale saliency extraction in infrared imagery. Optik 125, 4039\u20134042 (2014). https:\/\/doi.org\/10.1016\/j.ijleo.2014.01.117","journal-title":"Optik"},{"key":"2797_CR9","doi-asserted-by":"crossref","unstructured":"Lore, K.G., Akintayo, A., Sarkar, S., et al.: LLNet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognit. 61, 650\u2013662 (2018). arxiv:1808.04560","DOI":"10.1016\/j.patcog.2016.06.008"},{"key":"2797_CR10","unstructured":"Wei, C., Wang, W., Yang, W., et al.: Deep Retinex decomposition for low-light enhancement[EB\/OL]. arXiv:1808.04560 (2018)"},{"key":"2797_CR11","doi-asserted-by":"publisher","unstructured":"Choi, Y., Kim, N., Hwang, S., Kweon, I.S.: Thermal image enhancement using convolutional neural network. In: 2016 IEEE\/RSJ International conference on intelligent robots and systems (IROS), Daejeon, Korea (South), pp. 223\u2013230 (2016). https:\/\/doi.org\/10.1109\/IROS.2016.7759059.","DOI":"10.1109\/IROS.2016.7759059."},{"issue":"8","key":"2797_CR12","doi-asserted-by":"publisher","first-page":"2310","DOI":"10.1109\/TCSVT.2018.2864777","volume":"29","author":"Z He","year":"2019","unstructured":"He, Z., Tang, S., Yang, J., Cao, Y., Ying Yang, M., Cao, Y.: Cascaded deep networks with multiple receptive fields for infrared image super-resolution. IEEE Trans Circuits Syst. Video Technol. 29(8), 2310\u20132322 (2019). https:\/\/doi.org\/10.1109\/TCSVT.2018.2864777","journal-title":"IEEE Trans Circuits Syst. Video Technol."},{"key":"2797_CR13","doi-asserted-by":"publisher","unstructured":"Guo, C., Li, C., Guo, J., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, pp. 1777\u20131786 (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00185.","DOI":"10.1109\/CVPR42600.2020.00185."},{"key":"2797_CR14","doi-asserted-by":"publisher","unstructured":"Zhu, A., Zhang, L., Shen, Y., Ma, Y., Zhao, S., Zhou, Y.: Zero-shot restoration of underexposed images via robust retinex decomposition. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK, pp. 1\u20136 (2020). https:\/\/doi.org\/10.1109\/ICME46284.2020.9102962.","DOI":"10.1109\/ICME46284.2020.9102962."},{"key":"2797_CR15","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neucom.2018.11.081","volume":"332","author":"X Kuang","year":"2019","unstructured":"Kuang, X., Sui, X., Liu, Y., et al.: Single infrared image enhancement using a deep convolutional neural network. Neurocomputing 332, 119\u2013128 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2018.11.081","journal-title":"Neurocomputing"},{"key":"2797_CR16","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","volume":"30","author":"Y Jiang","year":"2021","unstructured":"Jiang, Y., et al.: EnlightenGAN: Deep light enhancement without paired supervision. IEEE Trans. Image Process. 30, 2340\u20132349 (2021). https:\/\/doi.org\/10.1109\/TIP.2021.3051462","journal-title":"IEEE Trans. Image Process."},{"key":"2797_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105411","author":"H Cui","year":"2022","unstructured":"Cui, H., Li, J., Hua, Z., Fan, L.: TPET: Two-stage perceptual enhancement transformer network for low-light image enhancement. Eng. Appl. Artif. Intell. (2022). https:\/\/doi.org\/10.1016\/j.engappai.2022.105411","journal-title":"Eng. Appl. Artif. Intell."},{"key":"2797_CR18","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conf. Comput. Vis. Pattern Recog., pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"1\u20133","key":"2797_CR19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11263-017-1004-z","volume":"125","author":"S Xie","year":"2017","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125(1\u20133), 3\u201318 (2017)","journal-title":"Int. J. Comput. Vis."},{"key":"2797_CR20","doi-asserted-by":"publisher","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Int. Conf. Learn. Represent (2015). https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"2797_CR21","doi-asserted-by":"publisher","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","DOI":"10.1109\/TPAMI.2016.2572683"},{"issue":"8","key":"2797_CR22","doi-asserted-by":"publisher","first-page":"1939","DOI":"10.1109\/TPAMI.2018.2878849","volume":"41","author":"Y Liu","year":"2019","unstructured":"Liu, Y., et al.: Richer convolutional features for edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1939\u20131946 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2018.2878849","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2797_CR23","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, F., Mei, K., Zhang, G.: Multi-scale residual network for image super-resolution. In: ECCV, pp. 517\u2013532 (2018)","DOI":"10.1007\/978-3-030-01237-3_32"},{"key":"2797_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2797_CR25","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol. 9351. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2797_CR26","doi-asserted-by":"publisher","unstructured":"Chen, G.-H., Yang, C.-L., Xie, S.-L.: Gradient-based structural similarity for image quality assessment. In: 2006 International Conference on Image Processing, Atlanta, GA, USA, pp. 2929\u20132932 (2006). https:\/\/doi.org\/10.1109\/ICIP.2006.313132","DOI":"10.1109\/ICIP.2006.313132"},{"issue":"3","key":"2797_CR27","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/LSP.2018.2792050","volume":"25","author":"C Li","year":"2018","unstructured":"Li, C., Guo, J., Guo, C.: Emerging from water: underwater image color correction based on weakly supervised color transfer. IEEE Signal Process. Lett. 25(3), 323\u2013327 (2018). https:\/\/doi.org\/10.1109\/LSP.2018.2792050","journal-title":"IEEE Signal Process. Lett."},{"key":"2797_CR28","doi-asserted-by":"crossref","unstructured":"Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., Jia, J.: Underexposed photo enhancement using deep illumination estimation. In: CVPR, pp. 6849\u20136857 (2019)","DOI":"10.1109\/CVPR.2019.00701"},{"key":"2797_CR29","doi-asserted-by":"publisher","unstructured":"Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. In: BMVC (2018). https:\/\/doi.org\/10.48550\/arXiv.1808.04560","DOI":"10.48550\/arXiv.1808.04560"},{"key":"2797_CR30","doi-asserted-by":"publisher","unstructured":"IEEE OTCBVS WS Series Bench; Davis, J., Sharma, V.: Background-subtraction using contour-based fusion of thermal and visible imagery. Comput. Vis. Image Underst. 106(2\u20133), 162\u2013182 (2007). https:\/\/doi.org\/10.1016\/j.cviu.2006.06.010","DOI":"10.1016\/j.cviu.2006.06.010"},{"key":"2797_CR31","doi-asserted-by":"publisher","unstructured":"Toet, A.: TNO Image Fusion Dataset. figshare. Dataset (2014). https:\/\/doi.org\/10.6084\/m9.figshare.1008029.v2","DOI":"10.6084\/m9.figshare.1008029.v2"},{"issue":"2","key":"2797_CR32","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2017","unstructured":"Guo, X., Li, Y., Ling, H.: LIME: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process 26(2), 982\u2013993 (2017). https:\/\/doi.org\/10.1109\/TIP.2016.2639450","journal-title":"IEEE Trans. Image Process"},{"key":"2797_CR33","doi-asserted-by":"publisher","first-page":"7984","DOI":"10.1109\/TIP.2020.3008396","volume":"29","author":"L-W Wang","year":"2020","unstructured":"Wang, L.-W., Liu, Z.-S., Siu, W.-C., Lun, D.P.K.: Lightening network for low-light image enhancement. IEEE Trans. Image Process. 29, 7984\u20137996 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.3008396","journal-title":"IEEE Trans. Image Process."},{"key":"2797_CR34","unstructured":"Zhang, F., Shao, Y., Sun, Y., Zhu, K., Gao, C., Sang, N.: Unsupervised low-light image enhancement via histogram equalization prior. arXiv:2112.01766 (2021)"},{"issue":"12","key":"2797_CR35","doi-asserted-by":"publisher","first-page":"4965","DOI":"10.1109\/TIP.2015.2474701","volume":"24","author":"X Fu","year":"2015","unstructured":"Fu, X., Liao, Y., Zeng, D., Huang, Y., Zhang, X.-P., Ding, X.: A probabilistic method for image enhancement with simultaneous illumination and reflectance estimation. IEEE Trans. Image Process. 24(12), 4965\u20134977 (2015). https:\/\/doi.org\/10.1109\/TIP.2015.2474701","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"2797_CR36","doi-asserted-by":"publisher","first-page":"4225","DOI":"10.1109\/TPAMI.2021.3063604","volume":"44","author":"C Li","year":"2022","unstructured":"Li, C., Guo, C., Loy, C.C.: Learning to enhance low-light image via zero-reference deep curve estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4225\u20134238 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3063604","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2797_CR37","doi-asserted-by":"publisher","unstructured":"Ma, L., Ma, T., Liu, R., Fan, X., Luo, Z.: Toward fast, flexible, and robust low-light image enhancement. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, pp. 5627\u20135636 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00555","DOI":"10.1109\/CVPR52688.2022.00555"},{"key":"2797_CR38","doi-asserted-by":"publisher","unstructured":"Liu, R., Ma, L., Zhang, J., Fan, X., Luo, Z.: Retinex-inspired Unrolling with cooperative prior architecture search for low-light image enhancement. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 10556\u201310565 (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.01042","DOI":"10.1109\/CVPR46437.2021.01042"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02797-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-023-02797-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-023-02797-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T07:04:50Z","timestamp":1708412690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-023-02797-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["2797"],"URL":"https:\/\/doi.org\/10.1007\/s11760-023-02797-4","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"3 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}