{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:11:13Z","timestamp":1760145073307,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Postdoctoral Science Foundation under Grant","award":["2020M683522","2024JC- 494 YBMS-490"],"award-info":[{"award-number":["2020M683522","2024JC- 494 YBMS-490"]}]},{"name":"Natural Science Basic Research Program of Shanxi under Grant","award":["2020M683522","2024JC- 494 YBMS-490"],"award-info":[{"award-number":["2020M683522","2024JC- 494 YBMS-490"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Current challenges in visible and infrared image fusion include color information distortion, texture detail loss, and target edge blur. To address these issues, a fusion algorithm based on double-domain transform filter and nonlinear contrast transform feature extraction (DDCTFuse) is proposed. First, for the problem of incomplete detail extraction that exists in the traditional transform domain image decomposition, an adaptive high-pass filter is proposed to decompose images into high-frequency and low-frequency portions. Second, in order to address the issue of fuzzy fusion target caused by contrast loss during the fusion process, a novel feature extraction algorithm is devised based on a novel nonlinear transform function. Finally, the fusion results are optimized and color-corrected by our proposed spatial-domain logical filter, in order to solve the color loss and edge blur generated in the fusion process. To validate the benefits of the proposed algorithm, nine classical algorithms are compared on the LLVIP, MSRS, INO, and Roadscene datasets. The results of these experiments indicate that the proposed fusion algorithm exhibits distinct targets, provides comprehensive scene information, and offers significant image contrast.<\/jats:p>","DOI":"10.3390\/s24123949","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T08:06:06Z","timestamp":1718784366000},"page":"3949","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Infrared and Visible Image Fusion Algorithm Based on Double-Domain Transform Filter and Contrast Transform Feature Extraction"],"prefix":"10.3390","volume":"24","author":[{"given":"Xu","family":"Ma","sequence":"first","affiliation":[{"name":"College of Safety Science and Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"},{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianqi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Safety Science and Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianrui","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Electrical and Control Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Luo, Y., Wang, X., Wu, Y., and Shu, C. (2023). Infrared and Visible Image Homography Estimation Using Multiscale Generative Adversarial Network. Electronics, 12.","DOI":"10.3390\/electronics12040788"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ji, J., Zhang, Y., Lin, Z., Li, Y., Wang, C., Hu, Y., Huang, F., and Yao, J. (2022). Fusion of Infrared and Visible Images Based on Optimized Low-Rank Matrix Factorization with Guided Filtering. Electronics, 11.","DOI":"10.3390\/electronics11132003"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5678","DOI":"10.1109\/TIP.2021.3087412","article-title":"Multi-interactive dual-decoder for RGB-thermal salient object detection","volume":"30","author":"Tu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.comcom.2019.12.039","article-title":"Unmanned Aerial vehicle\u2019s runway landing system with efficient target detection by using morphological fusion for military surveillance system","volume":"151","author":"Nagarani","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Vidas, S., Moghadam, P., and Bosse, M. (2013, January 6\u201310). 3D thermal mapping of building interiors using an RGB-D and thermal camera. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630890"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dinh, P.H. (2021). Combining gabor energy with equilibrium optimizer algorithm for multi-modality medical image fusion. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102696"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","article-title":"Infrared and visible image fusion methods and applications: A survey","volume":"45","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, L., Zhang, Y., Dong, L., and Zheng, F. (2022). Infrared and visible image fusion algorithm based on spatial-domain and image features. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0278055"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Huang, Y., Bi, D., and Wu, D. (2018). Infrared and visible image fusion based on different constraints in the non-subsampled shearlet transform domain. Sensors, 18.","DOI":"10.3390\/s18041169"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1016\/j.asoc.2011.11.020","article-title":"Infrared and visible image fusion using fuzzy logic and population-based optimization","volume":"12","author":"Saeedi","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.infrared.2015.11.002","article-title":"An improved fusion algorithm for infrared and visible images based on multi-scale transform","volume":"74","author":"Li","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110214","DOI":"10.1109\/ACCESS.2020.3001974","article-title":"Infrared and visible image fusion based on a latent low-rank representation nested with multiscale geometric transform","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.ins.2019.08.066","article-title":"Infrared and visible image fusion based on target-enhanced multiscale transform decomposition","volume":"508","author":"Chen","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2075","DOI":"10.1117\/1.1303728","article-title":"Contrast-based image fusion using the discrete wavelet transform","volume":"39","author":"Pu","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_16","unstructured":"Li, C., Lei, L., and Zhang, X. (2020, January 1\u20133). Infrared and Visible Image Fusion Based on Morphological Image Enhancement of Dual-Tree Complex Wavelet. Proceedings of the Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery: Volume 2, Xi\u2019an, China."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.infrared.2016.02.005","article-title":"Infrared and visible image fusion scheme based on NSCT and low-level visual features","volume":"76","author":"Li","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_18","first-page":"297","article-title":"Infrared and visible image fusion via NSST and PCNN in multiscale morphological gradient domain","volume":"Volume 11353","author":"Tan","year":"2020","journal-title":"Proceedings of the Optics, Photonics and Digital Technologies for Imaging Applications VI"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/72.761706","article-title":"PCNN models and applications","volume":"10","author":"Johnson","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"179857","DOI":"10.1109\/ACCESS.2020.3028088","article-title":"An infrared and visible image fusion algorithm based on LSWT-NSST","volume":"8","author":"Junwu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Suryanarayana, G., Varadarajan, V., Pillutla, S.R., Nagajyothi, G., and Kotapati, G. (2022). Multiple Degradation Skilled Network for Infrared and Visible Image Fusion Based on Multi-Resolution SVD Updation. Mathematics, 10.","DOI":"10.3390\/math10183389"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.proeng.2010.11.045","article-title":"Multimodal medical image fusion based on IHS and PCA","volume":"7","author":"He","year":"2010","journal-title":"Procedia Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5872","DOI":"10.3390\/s120505872","article-title":"Improved image fusion method based on NSCT and accelerated NMF","volume":"12","author":"Wang","year":"2012","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.neucom.2016.07.015","article-title":"LRSR: Low-rank-sparse representation for subspace clustering","volume":"214","author":"Wang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1109\/LSP.2017.2704024","article-title":"Multiscale decomposition in low-rank approximation","volume":"24","author":"Abdolali","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/81.222795","article-title":"The CNN paradigm","volume":"40","author":"Chua","year":"1993","journal-title":"IEEE Trans. Circuits Syst. I Fundam. Theory Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/JSEN.2015.2478655","article-title":"Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform","volume":"16","author":"Bavirisetti","year":"2015","journal-title":"IEEE Sensors J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, H., and Wu, X.J. (2018). Infrared and visible image fusion using latent low-rank representation. arXiv.","DOI":"10.1109\/ICPR.2018.8546006"},{"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":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.inffus.2022.10.034","article-title":"DIVFusion: Darkness-free infrared and visible image fusion","volume":"91","author":"Tang","year":"2023","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","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","article-title":"RFN-Nest: An end-to-end residual fusion network for infrared and visible images","volume":"73","author":"Li","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","article-title":"Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network","volume":"82","author":"Tang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jia, X., Zhu, C., Li, M., Tang, W., and Zhou, W. (2021, January 11\u201317). LLVIP: A visible-infrared paired dataset for low-light vision. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00389"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","article-title":"U2Fusion: A Unified Unsupervised Image Fusion Network","volume":"44","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3949\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:00:39Z","timestamp":1760108439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,18]]},"references-count":37,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123949"],"URL":"https:\/\/doi.org\/10.3390\/s24123949","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,6,18]]}}}