{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T19:04:34Z","timestamp":1791918274035,"version":"4.3.4"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"NTNU Norwegian University of Science and Technology"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Low-dose computed tomography (LDCT) has gained considerable attention for its ability to minimize patients\u2019 exposure to radiation thereby reducing the associated cancer risks. However, this reduction in radiation dose often results in degraded image quality due to the presence of noise and artifacts. To address this challenge, the present study proposes an LDCT image denoising method that leverages a pixel-level nonlocal self-similarity (NSS) prior in combination with a nonlocal means algorithm. The NSS prior identifies similar pixels within non-local regions, which proves more feasible and effective than patch-based similarity in enhancing denoising performance. By utilizing this pixel-level prior, the method accurately estimates noise levels and subsequently applies a non-local Haar transform to execute the denoising process. Furthermore, the study incorporates an enhanced version of a recently proposed nonlocal means algorithm. This revised approach uses discrete neighbourhood filtering properties to enable efficient, vectorized, and parallel computation on modern shared-memory platforms thereby reducing computational complexity. Experimental evaluations on publicly available benchmark dataset NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge demonstrate that the proposed method effectively suppresses noise and artifacts while preserving critical image details. Both visual and quantitative comparisons confirm that this approach outperforms several state-of-the-art techniques in terms of image quality and denoising efficiency.<\/jats:p>","DOI":"10.1038\/s41598-025-10139-2","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T15:07:02Z","timestamp":1752246422000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Low-dose computed tomography image denoising using pixel level non-local self-similarity prior with non-local means for healthcare informatics"],"prefix":"10.1038","volume":"15","author":[{"given":"Dawa Chyophel","family":"Lepcha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhawna","family":"Goyal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayush","family":"Dogra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krunal","family":"Vaghela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashish","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. S. Ravi","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Durga Prasad","family":"Bavirisetti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"10139_CR1","unstructured":"Computed Tomography. Principles, design, artifacts, and recent Advances-Jiang Hsieh-Google books. (accessed 01 Aug 2024)."},{"key":"10139_CR2","doi-asserted-by":"publisher","unstructured":"Shangguan, H. et al. Low-dose CT statistical iterative reconstruction via modified MRF regularization. Comput. Methods Progr. Biomed. 123, 129\u2013141. https:\/\/doi.org\/10.1016\/J.CMPB.2015.10.004 (2016).","DOI":"10.1016\/J.CMPB.2015.10.004"},{"key":"10139_CR3","doi-asserted-by":"publisher","unstructured":"Kalra, M. K. et al. Strategies for CT Radiation Dose Optimization. 230(3), 619\u2013628. https:\/\/doi.org\/10.1148\/RADIOL.2303021726  (2004).","DOI":"10.1148\/RADIOL.2303021726"},{"key":"10139_CR4","doi-asserted-by":"publisher","unstructured":"Chen, M., Pu, Y. F. & Bai, Y. C. Low-dose CT image denoising using residual convolutional network with fractional TV loss. Neurocomputing. 452, 510\u2013520. https:\/\/doi.org\/10.1016\/J.NEUCOM.2020.10.004 (2021).","DOI":"10.1016\/J.NEUCOM.2020.10.004"},{"key":"10139_CR5","doi-asserted-by":"publisher","unstructured":"Yuan, Q. et al. Edge-preserving median filter and weighted coding with sparse nonlocal regularization for low-dose CT image denoising algorithm. J. Healthc. Eng. 2021. https:\/\/doi.org\/10.1155\/2021\/6095676  (2021).","DOI":"10.1155\/2021\/6095676"},{"key":"10139_CR6","doi-asserted-by":"publisher","unstructured":"Chyophel Lepcha, D., Goyal, B. & Dogra, A. Low-dose CT image denoising using sparse 3d transformation with probabilistic non-local means for clinical applications. 2023. https:\/\/doi.org\/10.1080\/13682199.2023.2176809 (2023).","DOI":"10.1080\/13682199.2023.2176809"},{"key":"10139_CR7","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1109\/TMI.2012.2187213","volume":"31","author":"M Balda","year":"2012","unstructured":"Balda, M., Hornegger, J. & Heismann, B. Ray contribution masks for structure adaptive sinogram filtering. IEEE Trans. Med. Imaging. 31, 1228\u20131239. https:\/\/doi.org\/10.1109\/TMI.2012.2187213 (2012).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10139_CR8","doi-asserted-by":"publisher","unstructured":"Chen, G. H., Tang, J. & Leng, S. Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. Med. Phys. 35(2), 660\u2013663. https:\/\/doi.org\/10.1118\/1.2836423 (2008).","DOI":"10.1118\/1.2836423"},{"issue":"9","key":"10139_CR9","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1109\/TMI.2012.2195669","volume":"31","author":"Q Xu","year":"2012","unstructured":"Xu, Q. et al. Low-dose X-ray CT reconstruction via dictionary learning. IEEE Trans. Med. Imaging. 31 (9), 1682\u20131697. https:\/\/doi.org\/10.1109\/TMI.2012.2195669 (2012).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10139_CR10","doi-asserted-by":"publisher","unstructured":"Kim, K. et al. Mar., Sparse-view spectral CT reconstruction using spectral patch-based low-rank penalty. IEEE Trans. Med. Imaging. 34(3), 748\u2013760. https:\/\/doi.org\/10.1109\/TMI.2014.2380993 (2015).","DOI":"10.1109\/TMI.2014.2380993"},{"key":"10139_CR11","doi-asserted-by":"publisher","unstructured":"Niu, S. et al. Sparse-view x-ray CT reconstruction via total generalized variation regularization. Phys. Med. Biol. 59 (12), 2997. https:\/\/doi.org\/10.1088\/0031-9155\/59\/12\/2997 (2014).","DOI":"10.1088\/0031-9155\/59\/12\/2997"},{"key":"10139_CR12","doi-asserted-by":"publisher","unstructured":"Kim, W., Jeon, S. Y., Byun, G., Yoo, H. & Choi, J. H. A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective. Biomed. Eng. Lett. 14 (6), 1153\u20131173. https:\/\/doi.org\/10.1007\/S13534-024-00419-7\/TABLES\/2 (2024).","DOI":"10.1007\/S13534-024-00419-7\/TABLES\/2"},{"key":"10139_CR13","doi-asserted-by":"publisher","unstructured":"Li, K. et al. Feb., Low-dose CT via convolutional neural network. Biomed. Opt. Express. 8(2), 679\u2013694. https:\/\/doi.org\/10.1364\/BOE.8.000679 (2017).","DOI":"10.1364\/BOE.8.000679"},{"key":"10139_CR14","doi-asserted-by":"publisher","unstructured":"Chen, H. et al. Low-dose CT with a residual encoder\u2013decoder convolutional neural network. IEEE Trans. Med. Imaging. 36 (12), 2524\u20132535. https:\/\/doi.org\/10.1109\/TMI.2017.2715284 (2017).","DOI":"10.1109\/TMI.2017.2715284"},{"key":"10139_CR15","unstructured":"Wu, D., Kim, K., Fakhri, G. E. & Li, Q. A cascaded convolutional neural network for X-ray Low-dose CT image denoising, May 2017, Accessed: Dec. 14, 2024. https:\/\/arxiv.org\/abs\/1705.04267v2."},{"key":"10139_CR16","doi-asserted-by":"publisher","unstructured":"Wolterink, J. M., Leiner, T., Viergever, M. A. & I\u0161gum, I. Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging. 36 (12), 2536\u20132545. https:\/\/doi.org\/10.1109\/TMI.2017.2708987 (2017).","DOI":"10.1109\/TMI.2017.2708987"},{"key":"10139_CR17","doi-asserted-by":"publisher","unstructured":"Yang, Q. et al. Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging. 37(6), 1348\u20131357. https:\/\/doi.org\/10.1109\/TMI.2018.2827462 (2018).","DOI":"10.1109\/TMI.2018.2827462"},{"key":"10139_CR18","doi-asserted-by":"publisher","unstructured":"Shan, H. et al. 3-D convolutional Encoder-Decoder network for Low-Dose CT via transfer learning from a 2-D trained network. IEEE Trans. Med. Imaging. 37 (6), 1522\u20131534. https:\/\/doi.org\/10.1109\/TMI.2018.2832217 (2018).","DOI":"10.1109\/TMI.2018.2832217"},{"key":"10139_CR19","doi-asserted-by":"publisher","unstructured":"Fan, F. et al. Quadratic autoencoder (Q-AE) for low-dose CT denoising. IEEE Trans. Med. Imaging. 39 (6), 2035\u20132050. https:\/\/doi.org\/10.1109\/TMI.2019.2963248 (2020).","DOI":"10.1109\/TMI.2019.2963248"},{"key":"10139_CR20","doi-asserted-by":"publisher","unstructured":"Zhao, T., Hoffman, J., McNitt-Gray, M. & Ruan, D. Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics. Med. Phys. 46(1), 190\u2013198. https:\/\/doi.org\/10.1002\/MP.13252 (2019).","DOI":"10.1002\/MP.13252"},{"key":"10139_CR21","doi-asserted-by":"publisher","unstructured":"Trung, N. T., Hoan, T. D., Trung, N. L. & Luong, M. Low-dose CT image denoising using image decomposition and sparse representation. REV J. Electron. Commun. 9, 3\u20134. https:\/\/doi.org\/10.21553\/REV-JEC.238 (2020).","DOI":"10.21553\/REV-JEC.238"},{"key":"10139_CR22","doi-asserted-by":"publisher","unstructured":"Li, S. & Wang, G. Low-dose CT image denoising using parallel-clone networks. https:\/\/doi.org\/10.48550\/arxiv.2005.06724 (2020).","DOI":"10.48550\/arxiv.2005.06724"},{"key":"10139_CR23","doi-asserted-by":"publisher","unstructured":"Zhang, J. et al. Task-oriented low-dose CT image denoising. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12906 LNCS, 441\u2013450. https:\/\/doi.org\/10.1007\/978-3-030-87231-1_43\/COVER (2021).","DOI":"10.1007\/978-3-030-87231-1_43\/COVER"},{"key":"10139_CR24","doi-asserted-by":"publisher","unstructured":"Bai, T., Wang, B., Nguyen, D. & Jiang, S. Probabilistic self-learning framework for low-dose CT denoising. Med. Phys. 48 (5), 2258\u20132270. https:\/\/doi.org\/10.1002\/MP.14796 (2021).","DOI":"10.1002\/MP.14796"},{"key":"10139_CR25","doi-asserted-by":"publisher","unstructured":"Trung, N. T., Trinh, D. H., Trung, N. L. & Luong, M. Low-dose CT image denoising using deep convolutional neural networks with extended receptive fields. In Signal, Image and Video Processing 2022, 1\u20139. https:\/\/doi.org\/10.1007\/S11760-022-02157-8 (2022).","DOI":"10.1007\/S11760-022-02157-8"},{"key":"10139_CR26","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.2202.13517","author":"D Wang","year":"2022","unstructured":"Wang, D. et al. CTformer: Convolution-free Token2Token dilated vision transformer for low-dose CT denoising. Feb https:\/\/doi.org\/10.48550\/arxiv.2202.13517 (2022).","journal-title":"Feb"},{"key":"10139_CR27","doi-asserted-by":"publisher","unstructured":"Khmag, A. & Kamarudin, N. Natural image deblurring using recursive deep convolutional neural network (R-DbCNN) and second-generation wavelets. In Proceedings of the IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019, 285\u2013290. https:\/\/doi.org\/10.1109\/ICSIPA45851.2019.8977756 (2019).","DOI":"10.1109\/ICSIPA45851.2019.8977756"},{"key":"10139_CR28","doi-asserted-by":"publisher","unstructured":"Shukla, P. K. et al. Multiobjective genetic algorithm and convolutional neural network based COVID-19 identification in chest X-ray images. Math. Probl. Eng. 2021. https:\/\/doi.org\/10.1155\/2021\/7804540 (2021).","DOI":"10.1155\/2021\/7804540"},{"key":"10139_CR29","doi-asserted-by":"publisher","unstructured":"Lilhore, U. K. et al. Hybrid model for detection of cervical Cancer using causal analysis and machine learning techniques. Comput. Math. Methods Med. 2022 https:\/\/doi.org\/10.1155\/2022\/4688327 (2022).","DOI":"10.1155\/2022\/4688327"},{"key":"10139_CR30","doi-asserted-by":"publisher","unstructured":"Dhiman, P. et al. A novel deep learning model for detection of severity level of the disease in citrus fruits, Electronics. 11, 495. https:\/\/doi.org\/10.3390\/ELECTRONICS11030495 (2022).","DOI":"10.3390\/ELECTRONICS11030495"},{"key":"10139_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3219856","author":"L Yang","year":"2022","unstructured":"Yang, L. et al. Low-dose CT denoising via sinogram inner-structure transformer. IEEE Trans. Med. Imaging. https:\/\/doi.org\/10.1109\/TMI.2022.3219856 (2022).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10139_CR32","doi-asserted-by":"publisher","unstructured":"Han, Z. et al. A dual-encoder-single-decoder based low-dose CT denoising network, IEEE J. Biomed. Health Inform. 26(7), 3251\u20133260. https:\/\/doi.org\/10.1109\/JBHI.2022.3155788 (2022).","DOI":"10.1109\/JBHI.2022.3155788"},{"key":"10139_CR33","doi-asserted-by":"publisher","unstructured":"Kim, W., Lee, J., Kang, M., Kim, J. S. & Choi, J. H. Wavelet subband-specific learning for low-dose computed tomography denoising, PLoS One. 17(9), e0274308. https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0274308 (2022).","DOI":"10.1371\/JOURNAL.PONE.0274308"},{"key":"10139_CR34","doi-asserted-by":"publisher","unstructured":"Li, S. et al. An adaptive self-guided wavelet convolutional neural network with compound loss for low-dose CT denoising. Biomed. Signal. Process. Control. 75, 103543. https:\/\/doi.org\/10.1016\/J.BSPC.2022.103543 (2022).","DOI":"10.1016\/J.BSPC.2022.103543"},{"key":"10139_CR35","doi-asserted-by":"publisher","unstructured":"Li, Z. et al. Multi-Scale feature fusion network for Low-Dose CT denoising. J. Digit. Imaging. 1\u201318. https:\/\/doi.org\/10.1007\/S10278-023-00805-0\/METRICS (2023).","DOI":"10.1007\/S10278-023-00805-0\/METRICS"},{"key":"10139_CR36","doi-asserted-by":"publisher","unstructured":"Wang, J. et al. Jan., Domain-adaptive denoising network for low-dose CT via noise estimation and transfer learning, Med. Phys. 50(1), 74\u201388. https:\/\/doi.org\/10.1002\/MP.15952 (2023).","DOI":"10.1002\/MP.15952"},{"key":"10139_CR37","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/TCI.2023.3241546","volume":"9","author":"R Yan","year":"2023","unstructured":"Yan, R. et al. Image denoising for low-dose CT via convolutional dictionary learning and neural network. IEEE Trans. Comput. Imaging. 9, 83\u201393. https:\/\/doi.org\/10.1109\/TCI.2023.3241546 (2023).","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"10139_CR38","doi-asserted-by":"publisher","unstructured":"Isola, P., Zhu, J. Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. In Proceedings\u201430th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, vol. 2017-January, 5967\u20135976. https:\/\/doi.org\/10.48550\/arxiv.1611.07004 (2017).","DOI":"10.48550\/arxiv.1611.07004"},{"key":"10139_CR39","doi-asserted-by":"publisher","unstructured":"Yi, X. & Babyn, P. Sharpness-aware low-dose CT denoising using conditional generative adversarial network. J. Digit. Imaging. 31(5), 655\u2013669. https:\/\/doi.org\/10.1007\/S10278-018-0056-0 (2018).","DOI":"10.1007\/S10278-018-0056-0"},{"key":"10139_CR40","doi-asserted-by":"publisher","first-page":"67519","DOI":"10.1109\/ACCESS.2020.2986388","volume":"8","author":"Y Ma","year":"2020","unstructured":"Ma, Y. et al. Low-dose CT image denoising using a generative adversarial network with a hybrid loss function for noise learning. IEEE Access. 8, 67519\u201367529. https:\/\/doi.org\/10.1109\/ACCESS.2020.2986388 (2020).","journal-title":"IEEE Access."},{"key":"10139_CR41","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.1109\/TCI.2021.3129369","volume":"7","author":"T Kwon","year":"2021","unstructured":"Kwon, T. & Ye, J. C. Cycle-free cyclegan using invertible generator for unsupervised low-dose CT denoising. IEEE Trans. Comput. Imaging. 7, 1354\u20131368. https:\/\/doi.org\/10.1109\/TCI.2021.3129369 (2021).","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"10139_CR42","doi-asserted-by":"publisher","unstructured":"Du, W., Chen, H., Yang, H. & Zhang, Y. Disentangled generative adversarial network for low-dose CT. EURASIP J. Adv. Signal Process. 1, 1\u201316. https:\/\/doi.org\/10.1186\/S13634-021-00749-Z\/TABLES\/4 (2021).","DOI":"10.1186\/S13634-021-00749-Z\/TABLES\/4"},{"key":"10139_CR43","doi-asserted-by":"publisher","unstructured":"Huang, Z., Zhang, J., Zhang, Y. & Shan, H. Generative adversarial networks with dual-domain U-Net-based discriminators for low-dose CT denoising. IEEE Trans. Instrum. Meas. 71 https:\/\/doi.org\/10.1109\/TIM.2021.3128703 (2022).","DOI":"10.1109\/TIM.2021.3128703"},{"key":"10139_CR44","doi-asserted-by":"publisher","unstructured":"Lepcha, D. C. et al. A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing. PLoS One. 18 (9), e0291911. https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0291911 (2023).","DOI":"10.1371\/JOURNAL.PONE.0291911"},{"key":"10139_CR45","doi-asserted-by":"publisher","unstructured":"Han, Y. et al. LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning, Sci. Rep.  14(1), 1\u201317. https:\/\/doi.org\/10.1038\/s41598-024-68668-1 (2024).","DOI":"10.1038\/s41598-024-68668-1"},{"key":"10139_CR46","doi-asserted-by":"publisher","unstructured":"Wang, L. et al. A dual encoder LDCT image denoising model based on cross-scale skip connections. Neurocomputing. 613, 128741. https:\/\/doi.org\/10.1016\/J.NEUCOM.2024.128741 (2025).","DOI":"10.1016\/J.NEUCOM.2024.128741"},{"key":"10139_CR47","doi-asserted-by":"publisher","unstructured":"Li, Q. et al. Feb., Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network. Med. Phys. 51(2), 1289\u20131312. https:\/\/doi.org\/10.1002\/MP.16331 (2024).","DOI":"10.1002\/MP.16331"},{"key":"10139_CR48","doi-asserted-by":"publisher","first-page":"98693","DOI":"10.1109\/ACCESS.2024.3425606","volume":"12","author":"Y Wang","year":"2024","unstructured":"Wang, Y. et al. Scale-sensitive generative adversarial network for low-dose CT image denoising. IEEE Access. 12, 98693\u201398706. https:\/\/doi.org\/10.1109\/ACCESS.2024.3425606 (2024).","journal-title":"IEEE Access."},{"key":"10139_CR49","doi-asserted-by":"publisher","unstructured":"Buades, A., Coll, B. & Morel, J. M. A non-local algorithm for image denoising. In  Proceedings\u20142005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. II, 60\u201365. https:\/\/doi.org\/10.1109\/CVPR.2005.38 (2005).","DOI":"10.1109\/CVPR.2005.38"},{"key":"10139_CR50","doi-asserted-by":"publisher","unstructured":"Dabov, K., Foi, A., Katkovnik, V. & Egiazarian, K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080\u20132095. https:\/\/doi.org\/10.1109\/TIP.2007.901238 (2007).","DOI":"10.1109\/TIP.2007.901238"},{"key":"10139_CR51","doi-asserted-by":"publisher","unstructured":"Tian, C. et al. Deep learning on image denoising: an overview. Neural Netw. 131, 251\u2013275. https:\/\/doi.org\/10.1016\/J.NEUNET.2020.07.025 (2020).","DOI":"10.1016\/J.NEUNET.2020.07.025"},{"key":"10139_CR52","doi-asserted-by":"publisher","unstructured":"Khmag, A., Al Haddad, S. A. R., Ramlee, R. A., Kamarudin, N. & Malallah, F. L. Natural image noise removal using nonlocal means and hidden Markov models in transform domain. Visual Comput. 34 (12), 1661\u20131675. https:\/\/doi.org\/10.1007\/S00371-017-1439-9\/TABLES\/4 (2018).","DOI":"10.1007\/S00371-017-1439-9\/TABLES\/4"},{"key":"10139_CR53","doi-asserted-by":"publisher","unstructured":"Khmag, A. Additive Gaussian noise removal based on generative adversarial network model and semi-soft thresholding approach. Multimed. Tools Appl. 82 (5), 7757\u20137777. https:\/\/doi.org\/10.1007\/S11042-022-13569-6\/TABLES\/4 (2023).","DOI":"10.1007\/S11042-022-13569-6\/TABLES\/4"},{"key":"10139_CR54","doi-asserted-by":"publisher","unstructured":"Buades, A., Coll, B. & Morel, J. M. Jul., A Review of image denoising algorithms, with a new one. 4(2), 490\u2013530. https:\/\/doi.org\/10.1137\/040616024 (2006).","DOI":"10.1137\/040616024"},{"key":"10139_CR55","doi-asserted-by":"publisher","unstructured":"Gilboa, G. & Osher, S. Nov., Nonlocal operators with applications to image processing. 7(3), 1005\u20131028. https:\/\/doi.org\/10.1137\/070698592 (2008).","DOI":"10.1137\/070698592"},{"key":"10139_CR56","doi-asserted-by":"publisher","first-page":"5121","DOI":"10.1109\/TIP.2020.2980116","volume":"29","author":"Y Hou","year":"2020","unstructured":"Hou, Y. et al. NLH: A blind pixel-level non-local method for real-world image denoising. IEEE Trans. Image Process. 29, 5121\u20135135. https:\/\/doi.org\/10.1109\/TIP.2020.2980116 (2020).","journal-title":"IEEE Trans. Image Process."},{"key":"10139_CR57","doi-asserted-by":"publisher","unstructured":"Nam, S., Hwang, Y., Matsushita, Y. & Kim, S. J. A holistic approach to cross-channel image noise modeling and its application to image denoising. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, 1683\u20131691. https:\/\/doi.org\/10.1109\/CVPR.2016.186 (2016).","DOI":"10.1109\/CVPR.2016.186"},{"key":"10139_CR58","doi-asserted-by":"publisher","unstructured":"Stankovir, R. S. & Falkowski, B. J. The Haar wavelet transform: its status and achievements. Comput. Electr. Eng. 29 (1), 25\u201344. https:\/\/doi.org\/10.1016\/S0045-7906(01)00011-8 (2003).","DOI":"10.1016\/S0045-7906(01)00011-8"},{"key":"10139_CR59","doi-asserted-by":"publisher","unstructured":"Sweldens, W. The lifting scheme: A custom-design construction of biorthogonal wavelets. Appl. Comput. Harmon Anal. 3(2), 186\u2013200. https:\/\/doi.org\/10.1006\/ACHA.1996.0015 (1996).","DOI":"10.1006\/ACHA.1996.0015"},{"key":"10139_CR60","doi-asserted-by":"publisher","unstructured":"McCollough, C. TU-FG-207A-04: overview of the low dose CT grand challenge. Med. Phys. 43, 3759\u20133760. https:\/\/doi.org\/10.1118\/1.4957556 (2016).","DOI":"10.1118\/1.4957556"},{"key":"10139_CR61","doi-asserted-by":"publisher","unstructured":"Xia, W., Shan, H., Wang, G. & Zhang, Y. Physics-\/model-based and data-driven methods for low-dose computed tomography: a survey, IEEE Signal Process. Mag. 40(2), 89\u2013100. https:\/\/doi.org\/10.1109\/MSP.2022.3204407 (2023).","DOI":"10.1109\/MSP.2022.3204407"},{"key":"10139_CR62","doi-asserted-by":"publisher","unstructured":"Li, Z. et al. Low-dose CT image denoising with improving WGAN and hybrid loss function. Comput. Math. Methods Med. 1, 2973108. https:\/\/doi.org\/10.1155\/2021\/2973108 (2021).","DOI":"10.1155\/2021\/2973108"},{"key":"10139_CR63","doi-asserted-by":"publisher","unstructured":"Lepcha, D. C., Goyal, B., Dogra, A., Sharma, K. P. & Gupta, D. N. A deep journey into image enhancement: A survey of current and emerging trends. Inform. Fusion. 93, 36\u201376. https:\/\/doi.org\/10.1016\/J.INFFUS.2022.12.012 (2023).","DOI":"10.1016\/J.INFFUS.2022.12.012"},{"key":"10139_CR64","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.inffus.2022.10.007","volume":"91","author":"DC Lepcha","year":"2023","unstructured":"Lepcha, D. C., Goyal, B., Dogra, A. & Goyal, V. Image super-resolution: A comprehensive review, recent trends, challenges and applications. Inform. Fusion. 91, 230\u2013260 (2023).","journal-title":"Inform. Fusion"},{"key":"10139_CR65","doi-asserted-by":"publisher","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861 (2004).","DOI":"10.1109\/TIP.2003.819861"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10139-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10139-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10139-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T05:45:59Z","timestamp":1757223959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-10139-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,11]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["10139"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-10139-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,11]]},"assertion":[{"value":"17 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"25095"}}