{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:27:29Z","timestamp":1771003649492,"version":"3.50.1"},"reference-count":23,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2024,6,17]]},"abstract":"<jats:p>The use of image fusion technology in the area of information processing is continuing to advance in depth thanks to ongoing hardware advancements and related research. An enhanced convolutional neural network approach is developed to fuse visible and infrared images, and image pre-processing is carried out utilising an image alignment method with edge detection in order to gain more accurate and trustworthy image information. The performance of the fast wavelet decomposition, convolutional neural network, and modified convolutional neural network techniques is compared and examined using four objective assessment criteria. The experimental findings demonstrated that the picture alignment was successful with an offset error of fewer than 3 pixels in the horizontal direction and an angle error of less than 0.3\u2218 in both directions. The revised convolutional neural network method increased the information entropy, mean gradient, standard deviation, and edge detection information by an average of 46.13%, 39.40%, 19.91%, and 3.72%. The runtime of the modified approach was lowered by 19.42% when compared to the convolutional neural network method, which enhanced the algorithm\u2019s performance and boosted the effectiveness of picture fusion. The image fusion accuracy reached 98.61%, indicating that the method has better fusion performance and is of practical value for improving image fusion quality.<\/jats:p>","DOI":"10.3233\/jcm-247272","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T12:18:10Z","timestamp":1718713090000},"page":"1665-1678","source":"Crossref","is-referenced-by-count":0,"title":["Internet street view image fusion method using convolutional neural network"],"prefix":"10.1177","volume":"24","author":[{"given":"Jing","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xiaoxuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yujing","family":"Wu","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JCM-247272_ref1","first-page":"5503","article-title":"Fusion of infrared and visible images using fuzzy based Siamese convolutional network","volume":"70","author":"Bhalla","year":"2022","journal-title":"CMC-Comput Mater Con."},{"issue":"9","key":"10.3233\/JCM-247272_ref2","doi-asserted-by":"crossref","first-page":"6880","DOI":"10.1109\/TIM.2020.2975405","article-title":"Laplacian rede composition for multimodal medical image fusion","volume":"69","author":"Li","year":"2020","journal-title":"IEEE T Instrum Meas."},{"issue":"11","key":"10.3233\/JCM-247272_ref3","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1080\/2150704X.2019.1649736","article-title":"Hyperspectral image classification based on convolutional neural network and random forest","volume":"10","author":"Wang","year":"2019","journal-title":"Remote Sens Lett."},{"issue":"10","key":"10.3233\/JCM-247272_ref4","doi-asserted-by":"crossref","first-page":"7079","DOI":"10.1021\/acs.analchem.0c00446","article-title":"Discovering new lipidomic features using cell type specific fluorophore expression to provide spatial and biological specificity in a multimodal workflow with MALDI imaging mass spectrometry","volume":"92","author":"Jones","year":"2020","journal-title":"Anal Chem."},{"issue":"14","key":"10.3233\/JCM-247272_ref5","doi-asserted-by":"crossref","first-page":"8062","DOI":"10.1109\/JSEN.2020.2981719","article-title":"Image dehazing by an artificial image fusion method based on adaptive structure decomposition","volume":"20","author":"Zheng","year":"2020","journal-title":"IEEE Sens J."},{"issue":"1","key":"10.3233\/JCM-247272_ref6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11760-022-02220-4","article-title":"A novel variational optimization model for medical CT and MR image fusion","volume":"17","author":"Wang","year":"2023","journal-title":"Signal Image Video P."},{"issue":"6","key":"10.3233\/JCM-247272_ref7","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1109\/TIM.2019.2928346","article-title":"An enhanced intelligent diagnosis method based on multi-sensor image fusion via improved deep learning network","volume":"69","author":"Wang","year":"2019","journal-title":"IEEE T Instrum Meas."},{"issue":"7","key":"10.3233\/JCM-247272_ref8","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"Swin Fusion: Cross-domain long-range learning for general image fusion via swin transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE-CAA J Automatic."},{"issue":"12","key":"10.3233\/JCM-247272_ref9","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","article-title":"Nest Fuse: 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 T Instrum Meas."},{"issue":"3","key":"10.3233\/JCM-247272_ref10","first-page":"1124","article-title":"Regularizing hyperspectral and multispectral image fusion by CNN denoiser","volume":"32","author":"Dian","year":"2020","journal-title":"IEEE T Neur Net Lear."},{"issue":"1","key":"10.3233\/JCM-247272_ref11","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1002\/ima.22294","article-title":"Multimodal neurological image fusion based on adaptive biological inspired neural model in nonsubsampled Shearlet domain","volume":"29","author":"Singh","year":"2019","journal-title":"Int J Imag Syst Tech"},{"issue":"22","key":"10.3233\/JCM-247272_ref12","doi-asserted-by":"crossref","first-page":"8839","DOI":"10.1080\/01431161.2020.1783713","article-title":"CNN-based fusion and classification of SAR and optical data","volume":"41","author":"Shakya","year":"2020","journal-title":"Int J Remote Sens."},{"key":"10.3233\/JCM-247272_ref13","doi-asserted-by":"crossref","unstructured":"Qiu T, Wen C, Xie K, Wen FQ, Sheng GQ, Tang XG. Efficient medical image enhancement based on CNN-FBB model. IET Image Process. 2019; 13(10): 1736-1744.","DOI":"10.1049\/iet-ipr.2018.6380"},{"issue":"1","key":"10.3233\/JCM-247272_ref14","first-page":"225","article-title":"A novel medical image fusion by combining TV-L1 decomposed textures based on adaptive weighting scheme","volume":"23","author":"Padmavathi","year":"2020","journal-title":"Eng Sci Technol."},{"issue":"10","key":"10.3233\/JCM-247272_ref15","doi-asserted-by":"crossref","first-page":"7660","DOI":"10.3934\/jimo.2023013","article-title":"Multi-focus image fusion based on HOSVD and parameter adaptive PCNN in fast local Laplacian filtering domain","volume":"19","author":"Li","year":"2023","journal-title":"J Ind Manag Optim."},{"issue":"06N07","key":"10.3233\/JCM-247272_ref16","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1142\/S0129054122420084","article-title":"Fuzzy dynamic obstacle avoidance algorithm for basketball robot based on multi-sensor data fusion technology","volume":"33","author":"Shi","year":"2022","journal-title":"Int J Found Comput S."},{"issue":"4","key":"10.3233\/JCM-247272_ref17","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1002\/ima.22436","article-title":"A novel approach in multimodality medical image fusion using optimal shearlet and deep learning","volume":"30","author":"Subbiah Parvathy","year":"2020","journal-title":"Int J Imag Syst Tech."},{"issue":"12","key":"10.3233\/JCM-247272_ref18","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1038\/s41592-019-0582-9","article-title":"Ilastik: interactive machine learning for (bio) image analysis","volume":"16","author":"Berg","year":"2019","journal-title":"Nat Methods."},{"issue":"1","key":"10.3233\/JCM-247272_ref19","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.aej.2021.05.004","article-title":"Modified phase correlation algorithm for image registration based on pyramid","volume":"61","author":"Li","year":"2022","journal-title":"Alex Eng J."},{"issue":"11","key":"10.3233\/JCM-247272_ref20","first-page":"2523","article-title":"Medical imaging fusion techniques: a survey benchmark analysis, open challenges and recommendations","volume":"10","author":"Khan","year":"2020","journal-title":"J Med Imag Health In."},{"issue":"1","key":"10.3233\/JCM-247272_ref21","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/TMI.2019.2923601","article-title":"Co-learning feature fusion maps from PET-CT images of lung cancer","volume":"39","author":"Kumar","year":"2019","journal-title":"IEEE T Med Imaging."},{"issue":"5","key":"10.3233\/JCM-247272_ref22","doi-asserted-by":"crossref","first-page":"993","DOI":"10.3934\/ipi.2022057","article-title":"Deblurring photographs of characters using deep neural networks","volume":"17","author":"Germer","year":"2023","journal-title":"Inverse Probl Imag."},{"issue":"21","key":"10.3233\/JCM-247272_ref23","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1016\/j.jcin.2019.06.047","article-title":"First use of futuristic image fusion technology during transcatheter aortic valve replacement","volume":"12","author":"Brouwer","year":"2019","journal-title":"JACC-Cardiovasc Inte."}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-247272","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:32:26Z","timestamp":1771000346000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-247272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,17]]},"references-count":23,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jcm-247272","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,17]]}}}