{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T14:01:25Z","timestamp":1781272885423,"version":"3.54.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T00:00:00Z","timestamp":1773446400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":90,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["62102373"],"award-info":[{"award-number":["62102373"]}]},{"name":"National Natural Science Foundation of China under Grant","award":["62272423"],"award-info":[{"award-number":["62272423"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["242102321034"],"award-info":[{"award-number":["242102321034"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["252102220031"],"award-info":[{"award-number":["252102220031"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["252102310486"],"award-info":[{"award-number":["252102310486"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["242102210048"],"award-info":[{"award-number":["242102210048"]}]},{"name":"Henan Province Key R and D Project","award":["241111210400"],"award-info":[{"award-number":["241111210400"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In recent years, low-dose computed tomography (LDCT) image denoising techniques for the femoral head have become a prominent research focus in medical image processing because they maintain diagnostic accuracy while reducing radiation exposure. However, current methods still face challenges in preserving edge structures and facilitating collaborative multi-domain feature modeling. To address these challenges, this paper proposes an end-to-end cascaded transformer network with edge enhancement for LDCT image denoising (CTNEE-Net), which employs a multi-stage learnable unfolding architecture to synergistically optimize denoising and edge enhancement specifically for femoral head LDCT images. The network introduces a Learnable Edge Feature Extraction Module (LEFEM) that adaptively enhances bone edge details while suppressing noise. It also incorporates a Hybrid Transformer Block (HTB) that integrates frequency-domain attention and spatial self-attention to improve global context modeling. Furthermore, an Advanced Edge-Aware Fusion Module (AEAM) is proposed, which utilizes a gating mechanism to enable cross-domain adaptive fusion of edge features and deep semantic features, thereby enhancing the recovery of fine structures. Experimental results on 3 mm slice data from the Mayo dataset show that CTNEE-Net achieves PSNR and SSIM values of 34.05 dB and 0.9229, respectively. Subjective evaluations on clinical data also demonstrate its superiority over comparative methods. By effectively addressing the insufficiency of multi-domain feature synergy, this study offers significant advantages in preserving edge structures and enhancing denoising performance, thereby providing a solution that combines high performance and clinical practicality for LDCT image post-processing.<\/jats:p>","DOI":"10.1007\/s44443-026-00653-2","type":"journal-article","created":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:33:18Z","timestamp":1773487998000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CTNEE-Net: A cascaded transformer network with edge enhancement for low-dose femoral head CT image denoising"],"prefix":"10.1007","volume":"38","author":[{"given":"Jie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongpeng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huayu","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyang","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,14]]},"reference":[{"key":"653_CR1","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision, pp 205\u2013218","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"653_CR2","doi-asserted-by":"crossref","unstructured":"Charbonnier P, Blanc-Feraud L, Aubert G, Barlaud M (1994) Two deterministic half-quadratic regularization algorithms for computed imaging. In: Proceedings of 1st international conference on image processing, pp 168\u2013172","DOI":"10.1109\/ICIP.1994.413553"},{"key":"653_CR3","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) Transunet: Transformers make strong encoders for medical image segmentation. arXiv:2102.04306"},{"key":"653_CR4","unstructured":"Chen K, Sun J, Shen J, Luo J, Zhang X, Pan X, Wu D, Zhao Y, Bento M, Ren Y, Pu X (2022) Gcn-mif: Graph convolutional network with multi-information fusion for low-dose ct denoising. arXiv:2105.07146"},{"key":"653_CR5","doi-asserted-by":"publisher","first-page":"13868","DOI":"10.1038\/s41598-017-13520-y","volume":"7","author":"Y Chen","year":"2017","unstructured":"Chen Y, Liu J, Xie L, Hu Y, Shu H, Luo L, Zhang L, Gui Z, Coatrieux G (2017) Discriminative prior-prior image constrained compressed sensing reconstruction for low-dose ct imaging. Sci Rep 7:13868","journal-title":"Sci Rep"},{"key":"653_CR6","doi-asserted-by":"crossref","unstructured":"Chen Z, Niu C, Gao Q, Wang G, Shan H (2024a) Lit-former: Linking in-plane and through-plane transformers for simultaneous ct image denoising and deblurring. IEEE Trans Med Imaging 43:1880\u20131894","DOI":"10.1109\/TMI.2024.3351723"},{"key":"653_CR7","unstructured":"Chen Z, Wu Z, Zamfir E, Zhang K, Zhang Y, Timofte R, Yang X, Yu H, Wan C, Hong Y et al (2024b) Ntire 2024 challenge on image super-resolution (x4): Methods and results. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6108\u20136132"},{"key":"653_CR8","first-page":"1460","volume-title":"2017 IEEE International Conference on Power","author":"K Chithra","year":"2017","unstructured":"Chithra K, Santhanam T (2017) Hybrid denoising technique for suppressing gaussian noise in medical images. 2017 IEEE International Conference on Power. Control, Signals and Instrumentation Engineering (ICPCSI), pp 1460\u20131463"},{"key":"653_CR9","unstructured":"Dey S, Goswami M, Sethi J, Pattnaik PK (2025) Hyb-kan vit: Hybrid kolmogorov-arnold networks augmented vision transformer. arXiv:2505.04740"},{"key":"653_CR10","doi-asserted-by":"publisher","first-page":"9960","DOI":"10.1109\/TPAMI.2021.3138787","volume":"44","author":"J Dong","year":"2021","unstructured":"Dong J, Roth S, Schiele B (2021) Dwdn: deep wiener deconvolution network for non-blind image deblurring. IEEE Trans Pattern Anal Mach Intell 44:9960\u20139976","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"653_CR11","doi-asserted-by":"publisher","first-page":"8776","DOI":"10.1002\/mp.17379","volume":"51","author":"E Eulig","year":"2024","unstructured":"Eulig E, Ommer B, Kachelrie\u00df M (2024) Benchmarking deep learning-based low-dose ct image denoising algorithms. Med Phys 51:8776\u20138788","journal-title":"Med Phys"},{"key":"653_CR12","unstructured":"Fein-Ashley J, Fein-Ashley J, Fein-Ashley B (2024) Diffusion models with anisotropic gaussian splatting for image inpainting. ArXiv arXiv:2412.01682"},{"key":"653_CR13","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1109\/TMI.2023.3320812","volume":"43","author":"Q Gao","year":"2023","unstructured":"Gao Q, Li Z, Zhang J, Zhang Y, Shan H (2023) Corediff: Contextual error-modulated generalized diffusion model for low-dose ct denoising and generalization. IEEE Trans Med Imaging 43:745\u2013759","journal-title":"IEEE Trans Med Imaging"},{"key":"653_CR14","doi-asserted-by":"crossref","unstructured":"Gao Q, Shan H (2022) Cocodiff: a contextual conditional diffusion model for low-dose ct image denoising. In: Developments in X-Ray Tomography XIV, pp 92\u201398","DOI":"10.1117\/12.2634939"},{"key":"653_CR15","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","volume":"7","author":"MH Guo","year":"2021","unstructured":"Guo MH, Cai JX, Liu ZN, Mu TJ, Martin RR, Hu SM (2021) Pct: Point cloud transformer. Computational visual media 7:187\u2013199","journal-title":"Computational visual media"},{"key":"653_CR16","unstructured":"Guo S, Yong H, Zhang X, Ma J, Zhang L (2023) Spatial-frequency attention for image denoising. arXiv preprint arXiv:2302.13598"},{"key":"653_CR17","first-page":"1","volume":"71","author":"Z Huang","year":"2021","unstructured":"Huang Z, Zhang J, Zhang Y, Shan H (2021) Du-gan: Generative adversarial networks with dual-domain u-net-based discriminators for low-dose ct denoising. IEEE Trans Instrum Meas 71:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"653_CR18","doi-asserted-by":"publisher","first-page":"6081","DOI":"10.1007\/s11760-024-03295-x","volume":"18","author":"L Jia","year":"2024","unstructured":"Jia L, He X, Huang A, Jia B, Wang X (2024) Highly efficient encoder-decoder network based on multi-scale edge enhancement and dilated convolution for ldct image denoising. SIViP 18:6081\u20136091","journal-title":"SIViP"},{"key":"653_CR19","doi-asserted-by":"crossref","unstructured":"Kang D, Slomka P, Nakazato R, Woo J, Berman DS, Kuo CCJ, Dey D (2013) Image denoising of low-radiation dose coronary ct angiography by an adaptive block-matching 3d algorithm. In: Medical Imaging 2013: Image Processing, pp 671\u2013676","DOI":"10.1117\/12.2006907"},{"key":"653_CR20","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1007\/s00034-023-02488-y","volume":"43","author":"J Kang","year":"2024","unstructured":"Kang J, Liu Y, Shu H, Guo N, Zhang Q, Li Z, Gui Z (2024) Edge protection and global attention mechanism densely connected convolutional network for ldct denoising. Circuits Systems Signal Process 43:941\u2013964","journal-title":"Circuits Systems Signal Process"},{"key":"653_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2023.168519","volume":"1055","author":"J Kang","year":"2023","unstructured":"Kang J, Liu Y, Shu H, Guo N, Zhang Q, Zhou Y, Gui Z (2023) Gradient extraction based multiscale dense cross network for ldct denoising. Nucl Instrum Methods Phys Res, Sect A 1055:168519","journal-title":"Nucl Instrum Methods Phys Res, Sect A"},{"key":"653_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108378","volume":"173","author":"J Kang","year":"2024","unstructured":"Kang J, Liu Y, Zhang P, Guo N, Wang L, Du Y, Gui Z (2024) Fsformer: A combined frequency separation network and transformer for ldct denoising. Comput Biol Med 173:108378","journal-title":"Comput Biol Med"},{"key":"653_CR23","doi-asserted-by":"crossref","unstructured":"Kouni V, Paraskevopoulos G, Rauhut H, Alexandropoulos GC (2022) Admm-dad net: A deep unfolding network for analysis compressed sensing. In: ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1506\u20131510","DOI":"10.1109\/ICASSP43922.2022.9747096"},{"key":"653_CR24","doi-asserted-by":"crossref","unstructured":"Liang T, Jin Y, Li Y, Wang T (2020) Edcnn: Edge enhancement-based densely connected network with compound loss for low-dose ct denoising. In: 2020 15th IEEE International conference on signal processing (ICSP), pp 193\u2013198","DOI":"10.1109\/ICSP48669.2020.9320928"},{"key":"653_CR25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s13534-023-00310-x","volume":"14","author":"X Long","year":"2024","unstructured":"Long X, Tian C (2024) Spatial and channel attention-based conditional wasserstein gan for direct and rapid image reconstruction in ultrasound computed tomography. Biomed Eng Lett 14:57\u201368","journal-title":"Biomed Eng Lett"},{"key":"653_CR26","doi-asserted-by":"publisher","first-page":"15617","DOI":"10.1007\/s10489-021-03038-2","volume":"52","author":"S Luo","year":"2022","unstructured":"Luo S, Zhang J, Xiao N, Qiang Y, Li K, Zhao J, Meng L, Song P (2022) Das-net: A lung nodule segmentation method based on adaptive dual-branch attention and shadow mapping. Appl Intell 52:15617\u201315631","journal-title":"Appl Intell"},{"key":"653_CR27","unstructured":"Luthra A, Sulakhe H, Mittal T, Iyer A, Yadav S (2021) Eformer: Edge enhancement based transformer for medical image denoising. arXiv preprint arXiv:2109.08044"},{"key":"653_CR28","doi-asserted-by":"crossref","unstructured":"Mou C, Wang Q, Zhang J (2022) Deep generalized unfolding networks for image restoration, in: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 17399\u201317410","DOI":"10.1109\/CVPR52688.2022.01688"},{"key":"653_CR29","doi-asserted-by":"crossref","unstructured":"Pan S, Li Y (2025) Burst denoising transformer with multi-task optical flow estimation. Neural Networks, 107696","DOI":"10.1016\/j.neunet.2025.107696"},{"key":"653_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.113055","volume":"314","author":"X Pei","year":"2025","unstructured":"Pei X, Huang Y, Su W, Zhu F, Liu Q (2025) Fftformer: a spatial-frequency noise aware cnn-transformer for low light image enhancement. Knowl-Based Syst 314:113055","journal-title":"Knowl-Based Syst"},{"key":"653_CR31","doi-asserted-by":"crossref","unstructured":"Seif G, Androutsos D (2018) Edge-based loss function for single image super-resolution. In: 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 1468\u20131472","DOI":"10.1109\/ICASSP.2018.8461664"},{"key":"653_CR32","doi-asserted-by":"publisher","first-page":"944","DOI":"10.3390\/bioengineering11090944","volume":"11","author":"Y Son","year":"2024","unstructured":"Son Y, Jeong S, Hong Y, Lee J, Jeon B, Choi H, Kim J, Shim H (2024) Improvement in image quality of low-dose ct of canines with generative adversarial network of anti-aliasing generator and multi-scale discriminator. Bioengineering 11:944","journal-title":"Bioengineering"},{"key":"653_CR33","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.mri.2024.01.007","volume":"107","author":"B Wang","year":"2024","unstructured":"Wang B, Lian Y, Xiong X, Zhou H, Liu Z, Zhou X (2024) Dct-net: Dual-domain cross-fusion transformer network for mri reconstruction. Magn Reson Imaging 107:69\u201379","journal-title":"Magn Reson Imaging"},{"key":"653_CR34","doi-asserted-by":"publisher","first-page":"065012","DOI":"10.1088\/1361-6560\/acc000","volume":"68","author":"D Wang","year":"2023","unstructured":"Wang D, Fan F, Wu Z, Liu R, Wang F, Yu H (2023) Ctformer: convolution-free token2token dilated vision transformer for low-dose ct denoising. Phys Med Biol 68:065012","journal-title":"Phys Med Biol"},{"key":"653_CR35","doi-asserted-by":"crossref","unstructured":"Wang D, Wu Z, Yu H (2021a) Ted-net: Convolution-free t2t vision transformer-based encoder-decoder dilation network for low-dose ct denoising. arXiv:2106.04650","DOI":"10.1007\/978-3-030-87589-3_43"},{"key":"653_CR36","doi-asserted-by":"crossref","unstructured":"Wang D, Wu Z, Yu H (2021b) Ted-net: Convolution-free t2t vision transformer-based encoder-decoder dilation network for low-dose ct denoising. In: Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12, pp 416\u2013425","DOI":"10.1007\/978-3-030-87589-3_43"},{"key":"653_CR37","doi-asserted-by":"crossref","unstructured":"Wang Y, Yang N, Li J (2025) Gan-based architecture for low-dose computed tomography imaging denoising. arXiv:2411.09512","DOI":"10.4108\/eai.21-11-2024.2354627"},{"key":"653_CR38","doi-asserted-by":"crossref","unstructured":"Wang Z, Cun X, Bao J, Zhou W, Liu J, Li H (2022) Uformer: A general u-shaped transformer for image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 17683\u201317693","DOI":"10.1109\/CVPR52688.2022.01716"},{"key":"653_CR39","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14113","volume":"24","author":"Z Wang","year":"2023","unstructured":"Wang Z, Liu M, Cheng X, Zhu J, Wang X, Gong H, Liu M, Xu L (2023) Self-adaption and texture generation: A hybrid loss function for low-dose ct denoising. J Appl Clin Med Phys 24:e14113","journal-title":"J Appl Clin Med Phys"},{"key":"653_CR40","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2017.2785879","volume":"37","author":"G Yang","year":"2018","unstructured":"Yang G, Yu S, Dong H, Slabaugh G, Dragotti PL, Ye X, Liu F, Arridge S, Keegan J, Guo Y, Firmin D (2018) Dagan: Deep de-aliasing generative adversarial networks for fast compressed sensing mri reconstruction. IEEE Trans Med Imaging 37:1310\u20131321","journal-title":"IEEE Trans Med Imaging"},{"key":"653_CR41","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/TMI.2022.3219856","volume":"42","author":"L Yang","year":"2022","unstructured":"Yang L, Li Z, Ge R, Zhao J, Si H, Zhang D (2022) Low-dose ct denoising via sinogram inner-structure transformer. IEEE Trans Med Imaging 42:910\u2013921","journal-title":"IEEE Trans Med Imaging"},{"key":"653_CR42","doi-asserted-by":"publisher","first-page":"3597","DOI":"10.1002\/mp.16175","volume":"50","author":"S Yang","year":"2023","unstructured":"Yang S, Pu Q, Lei C, Zhang Q, Jeon S, Yang X (2023) Low-dose ct denoising with a high-level feature refinement and dynamic convolution network. Med Phys 50:3597\u20133611","journal-title":"Med Phys"},{"key":"653_CR43","doi-asserted-by":"publisher","first-page":"2827","DOI":"10.1109\/TIP.2023.3274988","volume":"32","author":"D Ye","year":"2023","unstructured":"Ye D, Ni Z, Wang H, Zhang J, Wang S, Kwong S (2023) Csformer: Bridging convolution and transformer for compressive sensing. IEEE Trans Image Process 32:2827\u20132842","journal-title":"IEEE Trans Image Process"},{"key":"653_CR44","doi-asserted-by":"crossref","unstructured":"You D, Xie J, Zhang J (2021) Ista-net++: Flexible deep unfolding network for compressive sensing. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp 1\u20136","DOI":"10.1109\/ICME51207.2021.9428249"},{"key":"653_CR45","doi-asserted-by":"publisher","first-page":"2290","DOI":"10.1007\/s10278-023-00842-9","volume":"36","author":"J Yuan","year":"2023","unstructured":"Yuan J, Zhou F, Guo Z, Li X, Yu H (2023) Hcformer: hybrid cnn-transformer for ldct image denoising. J Digit Imaging 36:2290\u20132305","journal-title":"J Digit Imaging"},{"key":"653_CR46","doi-asserted-by":"crossref","unstructured":"Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang MH (2022) Restormer: Efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5728\u20135739","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"653_CR47","doi-asserted-by":"crossref","unstructured":"Zhang C, Zhang T, Li M, Peng C, Liu Z, Zheng J (2016) Low-dose ct reconstruction via l1 dictionary learning regularization using iteratively reweighted least-squares. Biomed Eng Online 15:1\u201321","DOI":"10.1186\/s12938-016-0193-y"},{"key":"653_CR48","doi-asserted-by":"crossref","unstructured":"Zhang J, Ghanem B (2017) Ista-net: Iterative shrinkage-thresholding algorithm inspired deep network for image compressive sensing. ArXiv arXiv:1706.07929","DOI":"10.1109\/CVPR.2018.00196"},{"key":"653_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107162","volume":"163","author":"J Zhang","year":"2023","unstructured":"Zhang J, Shangguan Z, Gong W, Cheng Y (2023) A novel denoising method for low-dose ct images based on transformer and cnn. Comput Biol Med 163:107162","journal-title":"Comput Biol Med"},{"key":"653_CR50","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017a) Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans Image Process 26:3142\u20133155","DOI":"10.1109\/TIP.2017.2662206"},{"key":"653_CR51","doi-asserted-by":"crossref","unstructured":"Zhang K, Zuo W, Gu S, Zhang L (2017b) Learning deep cnn denoiser prior for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3929\u20133938","DOI":"10.1109\/CVPR.2017.300"},{"key":"653_CR52","doi-asserted-by":"publisher","first-page":"3184","DOI":"10.3390\/electronics13163184","volume":"13","author":"W Zhang","year":"2024","unstructured":"Zhang W, Salmi A, Yang C, Jiang F (2024) Innovative noise extraction and denoising in low-dose ct using a supervised deep learning framework. Electronics 13:3184","journal-title":"Electronics"},{"key":"653_CR53","doi-asserted-by":"publisher","first-page":"093107","DOI":"10.1117\/1.OE.61.9.093107","volume":"61","author":"X Zhu","year":"2022","unstructured":"Zhu X, Han Z, Yuan M, Guo Q, Wang H, Song L (2022) Hformer: hybrid convolutional neural network transformer network for fringe order prediction in phase unwrapping of fringe projection. Opt Eng 61:093107\u2013093107","journal-title":"Opt Eng"},{"key":"653_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111285","volume":"161","author":"Y Zhu","year":"2025","unstructured":"Zhu Y, He Q, Yao Y, Teng Y (2025) Self-supervised noise2noise method utilizing corrupted images with a modular network for ldct denoising. Pattern Recogn 161:111285","journal-title":"Pattern Recogn"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-026-00653-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00653-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00653-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T13:38:41Z","timestamp":1781271521000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-026-00653-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":54,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["653"],"URL":"https:\/\/doi.org\/10.1007\/s44443-026-00653-2","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,14]]},"assertion":[{"value":"31 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"224"}}