{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T20:42:07Z","timestamp":1782938527870,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T00:00:00Z","timestamp":1719014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi Province Key R&amp;D Program Project","award":["2024GX-YBXM-507"],"award-info":[{"award-number":["2024GX-YBXM-507"]}]},{"name":"Shaanxi Province Key R&amp;D Program Project","award":["22JK0508"],"award-info":[{"award-number":["22JK0508"]}]},{"name":"Shaanxi Province Key R&amp;D Program Project","award":["YCS23214256"],"award-info":[{"award-number":["YCS23214256"]}]},{"name":"Special scientific research Project of Shaanxi Provincial Education Department","award":["2024GX-YBXM-507"],"award-info":[{"award-number":["2024GX-YBXM-507"]}]},{"name":"Special scientific research Project of Shaanxi Provincial Education Department","award":["22JK0508"],"award-info":[{"award-number":["22JK0508"]}]},{"name":"Special scientific research Project of Shaanxi Provincial Education Department","award":["YCS23214256"],"award-info":[{"award-number":["YCS23214256"]}]},{"name":"Innovation and Practical Ability Cultivation Program for Postgraduates of Xi\u2019an Shiyou University","award":["2024GX-YBXM-507"],"award-info":[{"award-number":["2024GX-YBXM-507"]}]},{"name":"Innovation and Practical Ability Cultivation Program for Postgraduates of Xi\u2019an Shiyou University","award":["22JK0508"],"award-info":[{"award-number":["22JK0508"]}]},{"name":"Innovation and Practical Ability Cultivation Program for Postgraduates of Xi\u2019an Shiyou University","award":["YCS23214256"],"award-info":[{"award-number":["YCS23214256"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming to address the issues of missing detailed information, the blurring of significant target information, and poor visual effects in current image fusion algorithms, this paper proposes an infrared and visible-light image fusion algorithm based on discrete wavelet transform and convolutional neural networks. Our backbone network is an autoencoder. A DWT layer is embedded in the encoder to optimize frequency-domain feature extraction and prevent information loss, and a bottleneck residual block and a coordinate attention mechanism are introduced to enhance the ability to capture and characterize the low- and high-frequency feature information; an IDWT layer is embedded in the decoder to achieve the feature reconstruction of the fused frequencies; the fusion strategy adopts the l1\u2212norm fusion strategy to integrate the encoder\u2019s output frequency mapping features; a weighted loss containing pixel loss, gradient loss, and structural loss is constructed for optimizing network training. DWT decomposes the image into sub-bands at different scales, including low-frequency sub-bands and high-frequency sub-bands. The low-frequency sub-bands contain the structural information of the image, which corresponds to the important target information, while the high-frequency sub-bands contain the detail information, such as edge and texture information. Through IDWT, the low-frequency sub-bands that contain important target information are synthesized with the high-frequency sub-bands that enhance the details, ensuring that the important target information and texture details are clearly visible in the reconstructed image. The whole process is able to reconstruct the information of different frequency sub-bands back into the image non-destructively, so that the fused image appears natural and harmonious visually. Experimental results on public datasets show that the fusion algorithm performs well according to both subjective and objective evaluation criteria and that the fused image is clearer and contains more scene information, which verifies the effectiveness of the algorithm, and the results of the generalization experiments also show that our network has good generalization ability.<\/jats:p>","DOI":"10.3390\/s24134065","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T06:59:58Z","timestamp":1719212398000},"page":"4065","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["DCFNet: Infrared and Visible Image Fusion Network Based on Discrete Wavelet Transform and Convolutional Neural Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Dan","family":"Wu","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710312, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6972-6555","authenticated-orcid":false,"given":"Yanzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710312, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710312, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710312, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guowang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710312, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.eij.2015.09.002","article-title":"Current trends in medical image registration and fusion","volume":"17","author":"Elmogy","year":"2016","journal-title":"Egypt. Inform. J."},{"key":"ref_2","first-page":"1","article-title":"GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion","volume":"70","author":"Ma","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103016","DOI":"10.1016\/j.cviu.2020.103016","article-title":"Infrared and visible image fusion via gradientlet filter","volume":"197\u2013198","author":"Ma","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ma, W., Wang, K., Li, J., Yang, S.X., Li, J., Song, L., and Li, Q. (2023). Infrared and Visible Image Fusion Technology and Application: A Review. Sensors, 23.","DOI":"10.3390\/s23020599"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108301","DOI":"10.1016\/j.patcog.2021.108301","article-title":"Privacy-aware supervised classification: An informative subspace based multi-objective approach","volume":"122","author":"Biswas","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.bspc.2017.02.005","article-title":"Medical image fusion based on sparse representation of classified image patches","volume":"34","author":"Zong","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.1364\/JOSAA.34.001400","article-title":"Infrared and visible image fusion via saliency analysis and local edge-preserving multi-scale decomposition","volume":"34","author":"Zhang","year":"2017","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TCI.2022.3151472","article-title":"Injected Infrared and Visible Image Fusion via L1 Decomposition Model and Guided Filtering","volume":"8","author":"Yan","year":"2022","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/JPROC.2018.2853589","article-title":"On the Applications of Robust PCA in Image and Video Processing","volume":"106","author":"Bouwmans","year":"2018","journal-title":"Proc. IEEE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/JSEN.2007.894926","article-title":"Region-Based Multimodal Image Fusion Using ICA Bases","volume":"7","author":"Cvejic","year":"2007","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1109\/ACCESS.2015.2430359","article-title":"A Survey of Sparse Representation: Algorithms and Applications","volume":"3","author":"Zhang","year":"2015","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.infrared.2017.02.005","article-title":"Infrared and visible image fusion based on visual saliency map and weighted least square optimization","volume":"82","author":"Ma","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TCOM.1983.1095851","article-title":"The Laplacian Pyramid as a Compact Image Code","volume":"31","author":"Burt","year":"1983","journal-title":"IEEE Trans. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.sigpro.2013.10.010","article-title":"Region level based multi-focus image fusion using quaternion wavelet and normalized cut","volume":"97","author":"Liu","year":"2014","journal-title":"Signal Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4733","DOI":"10.1109\/TIP.2020.2975984","article-title":"MDLatLRR: A novel decomposition method for infrared and visible image fusion","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","first-page":"33","article-title":"Pyramid methods in image processing","volume":"29","author":"Adelson","year":"1984","journal-title":"RCA Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1080\/01431169308904362","article-title":"The wavelet transform for the analysis of remotely sensed images","volume":"14","author":"Ranchin","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.inffus.2010.03.007","article-title":"Biological image fusion using a NSCT based variable-weight method","volume":"12","author":"Li","year":"2011","journal-title":"Inf. Fusion"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.inffus.2015.11.003","article-title":"Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with Gaussian and bilateral filters","volume":"30","author":"Zhou","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.neucom.2016.11.051","article-title":"A novel infrared and visible image fusion algorithm based on shift-invariant dual-tree complex shearlet transform and sparse representation","volume":"226","author":"Yin","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"33248","DOI":"10.1109\/ACCESS.2023.3263183","article-title":"MGFuse: An Infrared and Visible Image Fusion Algorithm Based on Multiscale Decomposition Optimization and Gradient-Weighted Local Energy","volume":"11","author":"Hao","year":"2023","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1049\/iet-ipr.2019.0948","article-title":"Medical fusion framework using discrete fractional wavelets and non-subsampled directional filter banks","volume":"14","author":"Kaur","year":"2020","journal-title":"IET Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/TIP.2016.2633863","article-title":"Perceptual Image Fusion Using Wavelets","volume":"26","author":"Hill","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Su, H., and Jung, C. (2018, January 20\u201324). Multi-Spectral Fusion and Denoising of RGB and NIR Images Using Multi-Scale Wavelet Analysis. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545108"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1850018","DOI":"10.1142\/S0219691318500182","article-title":"Infrared and visible image fusion with convolutional neural networks","volume":"16","author":"Liu","year":"2018","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, H., Wu, X.J., and Kittler, J. (2018, January 20\u201324). Infrared and Visible Image Fusion using a Deep Learning Framework. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8546006"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Prabhakar, K.R., Srikar, V.S., and Babu, R.V. (2017, January 22\u201329). DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.505"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103039","DOI":"10.1016\/j.infrared.2019.103039","article-title":"Infrared and visible image fusion with ResNet and zero-phase component analysis","volume":"102","author":"Li","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","article-title":"DenseFuse: A Fusion Approach to Infrared and Visible Images","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","article-title":"IFCNN: A general image fusion framework based on convolutional neural network","volume":"54","author":"Zhang","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","article-title":"NestFuse: An Infrared and Visible Image Fusion Architecture Based on Nest Connection and Spatial\/Channel Attention Models","volume":"69","author":"Li","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/5.488702","article-title":"Wavelets for a vision","volume":"84","author":"Mallat","year":"1996","journal-title":"Proc. IEEE"},{"key":"ref_35","unstructured":"Han, S., Srivastava, A., Hurwitz, C., Sattigeri, P., and Cox, D. (2020). not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, Q., Shen, L., Guo, S., and Lai, Z. (2020, January 14\u201319). Wavelet Integrated CNNs for Noise-Robust Image Classification. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105047","DOI":"10.1016\/j.autcon.2023.105047","article-title":"Crack assessment using multi-sensor fusion simultaneous localization and mapping (SLAM) and image super-resolution for bridge inspection","volume":"155","author":"Feng","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","article-title":"Infrared and visible image fusion via gradient transfer and total variation minimization","volume":"31","author":"Ma","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xu, S., Zhang, C., Liu, J., Zhang, J., and Li, P. (2020, January 11\u201317). DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan.","DOI":"10.24963\/ijcai.2020\/135"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2022.3216413","article-title":"SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2022.11.010","article-title":"MUFusion: A general unsupervised image fusion network based on memory unit","volume":"92","author":"Cheng","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.dib.2017.09.038","article-title":"The TNO Multiband Image Data Collection","volume":"15","author":"Toet","year":"2017","journal-title":"Data Brief"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Brown, M., and Susstrunk, S. (2011, January 20\u201325). Multi-spectral SIFT for scene category recognition. Proceedings of the CVPR 2011, Providence, RI, USA.","DOI":"10.1109\/CVPR.2011.5995637"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4065\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:02:52Z","timestamp":1760108572000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/13\/4065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,22]]},"references-count":43,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24134065"],"URL":"https:\/\/doi.org\/10.3390\/s24134065","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,22]]}}}