{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:46:25Z","timestamp":1761198385783,"version":"3.41.0"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T00:00:00Z","timestamp":1722556800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19592-z","type":"journal-article","created":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T04:12:33Z","timestamp":1722571953000},"page":"22269-22301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Performance analysis of multimodal medical image fusion using AMT-DWT-based pre-processing and customized CNN for denoising"],"prefix":"10.1007","volume":"84","author":[{"given":"Tanima","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayanthi","family":"N.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,2]]},"reference":[{"key":"19592_CR1","doi-asserted-by":"publisher","first-page":"101677","DOI":"10.1016\/j.bspc.2019.101677","volume":"56","author":"P Kandhway","year":"2020","unstructured":"Kandhway P, Bhandari AK, Singh A (2020) A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization. Biomed Signal Process Control 56:101677","journal-title":"Biomed Signal Process Control"},{"issue":"12","key":"19592_CR2","doi-asserted-by":"publisher","first-page":"4663","DOI":"10.1109\/TCSVT.2019.2960861","volume":"30","author":"K Srinivas","year":"2019","unstructured":"Srinivas K, Bhandari AK, Singh A (2019) Exposure- based energy curve equalization for enhancement of contrast distorted images. IEEE Trans Circuits Syst Video Technol 30(12):4663\u20134675","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"9","key":"19592_CR3","doi-asserted-by":"publisher","first-page":"2009","DOI":"10.1109\/TFUZZ.2019.2930028","volume":"28","author":"AK Bhandari","year":"2019","unstructured":"Bhandari AK, Shahnawazuddin S, Meena AK (2019) A Novel fuzzy clustering-based histogram model for image contrast enhancement. IEEE Trans Fuzzy Syst 28(9):2009\u20132021","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"19592_CR4","first-page":"2278","volume":"2","author":"A.H.M. Jaffar Iqbal Barbhuiya and K","year":"2013","unstructured":"A.H.M. Jaffar Iqbal Barbhuiya and K. Hemachandran, (2013) Wavelet transformations & its major applications in digital image processing. Int J Eng Res Technol 2(3):2278\u2013181","journal-title":"Int J Eng Res Technol"},{"issue":"2","key":"19592_CR5","doi-asserted-by":"publisher","first-page":"0013","DOI":"10.1049\/el.2010.2063","volume":"46","author":"Y Yang","year":"2010","unstructured":"Yang Y, Su Z, Sun L (2010) Medical image enhancement algorithm based on wavelet transform. Electron Lett 46(2):0013\u20135194","journal-title":"Electron Lett"},{"key":"19592_CR6","doi-asserted-by":"crossref","unstructured":"Kaur R, Kaur S (2016) Comparison of contrast enhancement techniques for medical image. 2016 conference on emerging devices and smart systems (ICEDSS). IEEE","DOI":"10.1109\/ICEDSS.2016.7587782"},{"issue":"5","key":"19592_CR7","first-page":"414","volume":"4","author":"S Kaur","year":"2015","unstructured":"Kaur S, Kaur P (2015) Review and analysis of various image enhancement techniques. Int J Computer Appl Technol Res 4(5):414","journal-title":"Int J Computer Appl Technol Res"},{"key":"19592_CR8","doi-asserted-by":"crossref","unstructured":"Kong W, Li C, Lei\u00a0Y (2022) Multimodal medical image fusion using convolutional neural network and extreme learning machine. Front Neurorobot","DOI":"10.3389\/fnbot.2022.1050981"},{"key":"19592_CR9","doi-asserted-by":"crossref","unstructured":"Lee J, Pant SR, Lee H-S (2015) An adaptive histogram equalization based local technique for contrast preserving image enhancement. Int J Fuzzy Log Intell Sys 15(1):35\u201344","DOI":"10.5391\/IJFIS.2015.15.1.35"},{"key":"19592_CR10","doi-asserted-by":"crossref","unstructured":"Chen ZY et al (2006) Gray-level grouping (GLG): an automatic method for optimized image contrast Enhancement-part I: the basic method. IEEE Trans Image Proc 15(8):2290\u20132302","DOI":"10.1109\/TIP.2006.875204"},{"issue":"4","key":"19592_CR11","first-page":"21","volume":"2","author":"R Dorothy","year":"2015","unstructured":"Dorothy R et al (2015) Image enhancement by histogram equalization. Int J Nano Corrosion Sci Eng 2(4):21\u201330","journal-title":"Int J Nano Corrosion Sci Eng"},{"key":"19592_CR12","doi-asserted-by":"crossref","unstructured":"Ooi CH, Pik Kong NS, Ibrahim\u00a0H (2009) Bi-histogram equalization with a plateau limit for digital image enhancement. IEEE Trans Consum Electron 55(4):2072\u20132080","DOI":"10.1109\/TCE.2009.5373771"},{"key":"19592_CR13","doi-asserted-by":"crossref","unstructured":"Sharma A, Khunteta A (2016) Satellite image enhancement using discrete wavelet transform, singular value decomposition and its noise performance analysis. International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE). IEEE","DOI":"10.1109\/ICMETE.2016.32"},{"key":"19592_CR14","doi-asserted-by":"crossref","unstructured":"Mustafa WA et al (2019) Image enhancement based on discrete cosine transforms (DCT) and discrete wavelet transform (DWT): A review. IOP Conf Ser Mater Sci Eng 557(1). IOP Publishing","DOI":"10.1088\/1757-899X\/557\/1\/012027"},{"key":"19592_CR15","doi-asserted-by":"crossref","unstructured":"Li Y et al (2021) Multimodal medical supervised image fusion method by CNN. Front Neurosci :303","DOI":"10.3389\/fnins.2021.638976"},{"key":"19592_CR16","unstructured":"Veshki FG et al (2021) Coupled feature learning for multimodal medical image fusion. arXiv preprint arXiv:2102.08641"},{"key":"19592_CR17","doi-asserted-by":"crossref","unstructured":"Almasri MM, Alajlan AM (2022) Artificial intelligence-based multimodal medical image fusion using hybrid S2 optimal CNN. Electronics\u00a011(14):2124","DOI":"10.3390\/electronics11142124"},{"key":"19592_CR18","doi-asserted-by":"crossref","unstructured":"Veshki FG et al (2022) Multimodal image fusion via coupled feature learning. Sig Process 200:108637","DOI":"10.1016\/j.sigpro.2022.108637"},{"key":"19592_CR19","doi-asserted-by":"crossref","unstructured":"Huang B et al (2020) A review of multimodal medical image fusion techniques. Comput Math Methods Med 2020","DOI":"10.1155\/2020\/8279342"},{"issue":"7","key":"19592_CR20","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1109\/TIP.2013.2244222","volume":"22","author":"S Li","year":"2013","unstructured":"Li S, Kang X, Jianwen Hu (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864\u20132875","journal-title":"IEEE Trans Image Process"},{"key":"19592_CR21","doi-asserted-by":"crossref","unstructured":"Du J et al (2021) Medical image fusion by combining parallel features on multi-scale local extrema scheme. Knowledge-Based Systems\u00a0113 (2016): 4\u201312. Intell Syst 7(5):2179\u20132198","DOI":"10.1016\/j.knosys.2016.09.008"},{"key":"19592_CR22","doi-asserted-by":"crossref","unstructured":"Liu Y, Liu S, Wang Z (2014) Medical image fusion by combining nonsubsampled contourlet transform and sparse representation. Chinese conference on pattern recognition. Springer, Berlin, Heidelberg","DOI":"10.1007\/978-3-662-45643-9_39"},{"key":"19592_CR23","doi-asserted-by":"crossref","unstructured":"Das S, Kundu MK (2012) NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency. Med Biol Eng Comput 50(10):1105\u20131114","DOI":"10.1007\/s11517-012-0943-3"},{"issue":"1","key":"19592_CR24","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11517-022-02697-8","volume":"61","author":"SI Ibrahim","year":"2023","unstructured":"Ibrahim SI, Makhlouf MA, El-Tawel GS (2023) Multimodal medical image fusion algorithm based on pulse coupled neural networks and nonsubsampled contourlet transform. Med Biol Eng Compu 61(1):155\u2013177","journal-title":"Med Biol Eng Compu"},{"issue":"12","key":"19592_CR25","doi-asserted-by":"publisher","first-page":"3347","DOI":"10.1109\/TBME.2013.2282461","volume":"60","author":"S Das","year":"2013","unstructured":"Das S, Kundu MK (2013) A neuro-fuzzy approach for medical image fusion. IEEE Trans Biomed Eng 60(12):3347\u20133353","journal-title":"IEEE Trans Biomed Eng"},{"issue":"1","key":"19592_CR26","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/TIM.2018.2838778","volume":"68","author":"M Yin","year":"2019","unstructured":"Yin M, Liu X, Liu Y, Chen X (2019) Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain. IEEE Trans Instrum Meas 68(1):49\u201364","journal-title":"IEEE Trans Instrum Meas"},{"key":"19592_CR27","doi-asserted-by":"crossref","unstructured":"Shreyamsha Kumar BK (2013) Multifocus and multispectral image fusion based on pixel significance using discrete cosine harmonic wavelet transform.\u00a0SIViP 7(6):1125\u20131143","DOI":"10.1007\/s11760-012-0361-x"},{"key":"19592_CR28","doi-asserted-by":"crossref","unstructured":"Shreyamsha Kumar BK (2015) Image fusion based on pixel significance using cross bilateral filter.\u00a0SIViP 9(5):1193\u20131204","DOI":"10.1007\/s11760-013-0556-9"},{"key":"19592_CR29","unstructured":"Fan F, Huang Y, Wang\u00a0L et al (2019) A semantic-based medical image fusion approach. http:\/\/arxiv.org\/abs\/1906.00225.00225"},{"key":"19592_CR30","doi-asserted-by":"crossref","unstructured":"Liu Y et al (2017) A medical image fusion method based on convolutional neural networks. 2017 20th international conference on information fusion (Fusion). IEEE","DOI":"10.23919\/ICIF.2017.8009769"},{"key":"19592_CR31","doi-asserted-by":"crossref","unstructured":"Yang Y et al (2010) Medical image fusion via an effective wavelet-based approach.\"\u00a0EURASIP journal on advances in signal processing\u00a02010: 1\u201313","DOI":"10.1155\/2010\/579341"},{"key":"19592_CR32","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1016\/j.ins.2017.12.008","volume":"430","author":"J Du","year":"2018","unstructured":"Du J, Li W, Xiao B (2018) Fusion of anatomical and functional images using parallel saliency features. Inf Sci 430:567\u2013576","journal-title":"Inf Sci"},{"key":"19592_CR33","unstructured":"Chac\u00f3n Get al (2019) A score function as quality measure for cardiac image enhancement techniques assessment. Revista Latinoamericana de Hipertensi\u00f3n\u00a014.2:180\u2013186"},{"key":"19592_CR34","doi-asserted-by":"crossref","unstructured":"Zhu Z et al (2019) A phase congruency and local Laplacian energy based multi-modality medical image fusion method in NSCT domain. IEEE Access\u00a07:20811\u201320824","DOI":"10.1109\/ACCESS.2019.2898111"},{"key":"19592_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2015.10.084","volume":"182","author":"B Yu","year":"2016","unstructured":"Yu B, Jia B, Ding L, Cai Z, Wu Q (2016) Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion. Neurocomputing 182:1\u20139","journal-title":"Neurocomputing"},{"key":"19592_CR36","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.neucom.2016.02.047","volume":"194","author":"J Du","year":"2016","unstructured":"Du J, Li W, Xiao B (2016) Union laplacian pyramid with multiple features for medical image fusion. Neurocomputing 194:326\u2013339","journal-title":"Neurocomputing"},{"key":"19592_CR37","doi-asserted-by":"crossref","unstructured":"Venkatesan B, Ragupathy US, Natarajan\u00a0I (2023) A review on multimodal medical image fusion towards future research. Multimed Tools Appl 82.5:7361\u20137382","DOI":"10.1007\/s11042-022-13691-5"},{"key":"19592_CR38","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1109\/LSP.2016.2618776","volume":"23","author":"Y Liu","year":"2016","unstructured":"Liu Y, Chen X, Ward RK, Wang ZJ (2016) Image fusion with convolutional sparse representation. IEEE Signal Process Lett 23:1882\u20131886","journal-title":"IEEE Signal Process Lett"},{"key":"19592_CR39","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/LSP.2019.2895749","volume":"26","author":"Y Liu","year":"2019","unstructured":"Liu Y, Chen X, Ward RK, Wang ZJ (2019) Medical image fusion via convolutional sparsity based morphological component analysis. IEEE Signal Process Lett 26:485\u2013489","journal-title":"IEEE Signal Process Lett"},{"key":"19592_CR40","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.infrared.2016.02.005","volume":"76","author":"H Li","year":"2016","unstructured":"Li H, Qiu H, Yu Z, Zhang Y (2016) Infrared and visible image fusion scheme based on NSCT and low-level visual features. Infrared Phys Technol 76:174\u2013184","journal-title":"Infrared Phys Technol"},{"key":"19592_CR41","doi-asserted-by":"crossref","unstructured":"Budhiraja S (2015) Multimodal medical image fusion using modified fusion rules and guided filter. In\u00a0International Conference on Computing, Communication & Automation\u00a0(pp. 1067\u20131072). IEEE","DOI":"10.1109\/CCAA.2015.7148564"},{"key":"19592_CR42","doi-asserted-by":"crossref","unstructured":"Metwalli MR et al (2009) Image fusion based on principal component analysis and high-pass filter. 2009 International Conference on Computer Engineering & Systems. IEEE","DOI":"10.1109\/ICCES.2009.5383308"},{"key":"19592_CR43","doi-asserted-by":"crossref","unstructured":"Muzammil SR et al (2020) CSID: A novel multimodal image fusion algorithm for enhanced clinical diagnosis. Diagnostics\u00a010.11:904","DOI":"10.3390\/diagnostics10110904"},{"key":"19592_CR44","doi-asserted-by":"crossref","unstructured":"Yang Y (2020) A novel DWT based multi-focus image fusion method. Procedia Eng 24:177\u2013181. [CrossRef] Diagnostics 2020, 10, 904 21 of 22","DOI":"10.1016\/j.proeng.2011.11.2622"},{"issue":"5","key":"19592_CR45","doi-asserted-by":"publisher","first-page":"2179","DOI":"10.1007\/s40747-021-00428-4","volume":"7","author":"AE Ilesanmi","year":"2021","unstructured":"Ilesanmi AE, Ilesanmi TO (2021) Methods for image denouncing using convolutional neural network: a review. Complex Intelligent Systems 7(5):2179\u20132198","journal-title":"Complex Intelligent Systems"},{"key":"19592_CR46","doi-asserted-by":"crossref","unstructured":"Kligvasser I, Rott Shaham T, Michaeli T (2018) xUnit: Learning a spatial activation function for efficient image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2018.00258"},{"key":"19592_CR47","unstructured":"Introduction to Neural networks using Matlab 6.0 reviews and ratings ny S.N Sivanandam, S Sumathi and S.N Deepa by Tata Mc Graw Hill"},{"key":"19592_CR48","doi-asserted-by":"crossref","unstructured":"Huang W et al (2022) A Two-level dynamic adaptive network for medical image fusion. IEEE Trans Instrum Meas 71:1\u201317","DOI":"10.1109\/TIM.2022.3169546"},{"key":"19592_CR49","doi-asserted-by":"crossref","unstructured":"Liu Y, Chen X, Cheng J, Peng H (2017) A medical image fusion method based on convolutional neural networks. In Proc 20th Int Conf Inf (Fusion), pp 1070\u20131076","DOI":"10.23919\/ICIF.2017.8009769"},{"key":"19592_CR50","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","volume":"54","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Liu Y, Sun P, Yan H, Zhao X, Zhang L (2020) IFCNN: A general image fusion framework based on convolutional neural network. Inf Fusion 54:99\u2013118","journal-title":"Inf Fusion"},{"issue":"7","key":"19592_CR51","first-page":"12797","volume":"34","author":"H Zhang","year":"2020","unstructured":"Zhang H, Xu H, Xiao Y, Guo X, Ma J (2020) Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity. Proc AAAI Conf Artif Intell 34(7):12797\u201312804","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"1","key":"19592_CR52","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2022","unstructured":"Xu H, Ma J, Jiang J, Guo X, Ling H (2022) U2Fusion: A unified unsupervised image fusion network. IEEE Trans Pattern Anal Mach Intell 44(1):502\u2013518","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"13","key":"19592_CR53","doi-asserted-by":"publisher","first-page":"3521","DOI":"10.1073\/pnas.1611835114","volume":"114","author":"K James","year":"2017","unstructured":"James K et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Nat Acad Sci USA 114(13):3521\u20133526","journal-title":"Proc Nat Acad Sci USA"},{"key":"19592_CR54","unstructured":"Houlsby N et al (2019) Parameter-efficient transfer learning for NLP. In Proc Int Conf Mach Learn :2790\u20132799"},{"issue":"8","key":"19592_CR55","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1038\/s42256-019-0080-x","volume":"1","author":"G Zeng","year":"2019","unstructured":"Zeng G, Chen Y, Cui B, Yu S (2019) Continual learning of contextdependent processing in neural networks. Nature Mach Intell 1(8):364\u2013372","journal-title":"Nature Mach Intell"},{"key":"19592_CR56","doi-asserted-by":"crossref","unstructured":"Peng L et al (2022) Rethinking transfer learning for medical image classification. medRxiv :2022\u201311","DOI":"10.1101\/2022.11.26.22282782"},{"key":"19592_CR57","doi-asserted-by":"crossref","unstructured":"Banerjee I et al (2018) Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma. Comput Med Imaging Graph 65(2018):167\u2013175","DOI":"10.1016\/j.compmedimag.2017.05.002"},{"key":"19592_CR58","doi-asserted-by":"crossref","unstructured":"Hadi HA, Ziad SM (2020) Fusion of the multimodal medical images to enhance the quality using discrete wavelet transform. IOP Conf Ser Mater Sci Eng 745.(1). IOP Publishing","DOI":"10.1088\/1757-899X\/745\/1\/012036"},{"key":"19592_CR59","doi-asserted-by":"crossref","unstructured":"Jose J et al (2021)An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion. Biomed Signal Process Cont 66(2021):102480","DOI":"10.1016\/j.bspc.2021.102480"},{"issue":"14","key":"19592_CR60","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.3390\/diagnostics13142330","volume":"13","author":"M Haribabu","year":"2023","unstructured":"Haribabu M, Guruviah V (2023) An Improved multimodal medical image fusion approach using intuitionistic fuzzy set and intuitionistic fuzzy cross-correlation. Diagnostics 13(14):2330","journal-title":"Diagnostics"},{"issue":"1","key":"19592_CR61","doi-asserted-by":"publisher","first-page":"16726","DOI":"10.1038\/s41598-023-43873-6","volume":"13","author":"M Haribabu","year":"2023","unstructured":"Haribabu M, Guruviah V (2023) Enhanced multimodal medical image fusion based on Pythagorean fuzzy set: an innovative approach. Sci Rep 13(1):16726","journal-title":"Sci Rep"},{"key":"19592_CR62","unstructured":"Wankhede P, Das M, Gupta D, Radeva P, Bakde AM (2023) A new multimodal medical image fusion based on laplacian autoencoder with channel attention.\u00a0arXiv preprint arXiv:2310.11896"},{"key":"19592_CR63","doi-asserted-by":"publisher","first-page":"803724","DOI":"10.3389\/fncom.2021.803724","volume":"15","author":"S Liu","year":"2022","unstructured":"Liu S, Wang M, Yin L, Sun X, Zhang YD, Zhao J (2022) Two-scale multimodal medical image fusion based on structure preservation. Front Comput Neurosci 15:803724","journal-title":"Front Comput Neurosci"},{"issue":"9","key":"19592_CR64","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.3390\/electronics9091374","volume":"9","author":"R Fan","year":"2020","unstructured":"Fan R, Li X, Lee S, Li T, Zhang HL (2020) Smart image enhancement using CLAHE based on an F-shift transformation during decompression. Electronics 9(9):1374","journal-title":"Electronics"},{"key":"19592_CR65","doi-asserted-by":"crossref","unstructured":"Mishra SP, Sarkar U, Taraphder S, Datta S, Swain D, Saikhom R., ... Laishram M (2017) Multivariate statistical data analysis-principal component analysis (PCA).\u00a0Int J Livest Res 7(5):60\u201378","DOI":"10.5455\/ijlr.20170415115235"},{"issue":"7","key":"19592_CR66","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","volume":"26","author":"K Zhang","year":"2017","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans Image Process 26(7):3142\u20133155","journal-title":"IEEE Trans Image Process"},{"key":"19592_CR67","unstructured":"Bhat M, Karki MV (2017) Feature selection based on PCA and PSO for multimodal medical image fusion using DTCWT.\u00a0arXiv:1701.08918"},{"key":"19592_CR68","doi-asserted-by":"crossref","unstructured":"Hadi HA, Mohammed ZS (2020) Fusion of the multimodal medical images to enhance the quality using discrete wavelet transform. IOP Conf Ser Mater Sci Eng 745(1):012036. IOP Publishing","DOI":"10.1088\/1757-899X\/745\/1\/012036"},{"issue":"1","key":"19592_CR69","first-page":"654","volume":"13","author":"J Swarup","year":"2022","unstructured":"Swarup J, Sreedevi I (2022) DWT based historical image enhancement technique using adaptive gamma correction. J Algebraic Stat 13(1):654\u2013664","journal-title":"J Algebraic Stat"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19592-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19592-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19592-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T11:22:55Z","timestamp":1751455375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19592-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,2]]},"references-count":69,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["19592"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19592-z","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,8,2]]},"assertion":[{"value":"17 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors Tanima Ghosh and Dr. N.Jayanthi have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}