Li Y, Sixou B, Peyrin F. A review of the deep learning methods for medical images super resolution problems. IRBM. 2021;42(2):120–33. https://doi.org/10.1016/j.irbm.2020.08.004.

Article  CAS  Google Scholar 

Wang X, Yu K, Wu S, Gu J, Liu Y, et al. ESRGAN: enhanced super-resolution generative adversarial networks. In: Leal-Taixé L, Roth S, et al., editors. Computer vision—ECCV 2018 workshops. Cham: Springer; 2019. p. 63–79. https://doi.org/10.1007/978-3-030-11021-5_5.

Chapter  Google Scholar 

Nie W. BSD100, Set5, Set14, Urban100 datasets. https://figshare.com/articles/dataset/BSD100_Set5_Set14_Urban100/21586188.https://doi.org/10.6084/m9.figshare.21586188.v1. Accessed 07 Dec 2023.

Brain tumor MRI dataset. https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset. Accessed 07 Dec 2023.

Breast ultrasound images dataset. https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset. Accessed 07 Dec 2023.

Chest CT-scan images dataset. https://www.kaggle.com/datasets/mohamedhanyyy/chest-ctscan-images. Accessed 07 Dec 2023.

Chest X-ray images. https://www.kaggle.com/datasets/tolgadincer/labeled-chest-xray-images. Accessed 07 Dec 2023.

Alpaydin E. Machine learning. Cambridge: The MIT Press; 2016.

Google Scholar 

Li Z, Dewaraja YK, Fessler JA. Training End-to-End unrolled iterative neural networks for SPECT image reconstruction. IEEE Trans Radiat Plasma Med Sci. 2023;7(4):410–20. https://doi.org/10.1109/trpms.2023.3240934.

Article  PubMed  PubMed Central  Google Scholar 

He Z, Zhu YN, Chen Y, Chen Y, He Y, et al. A deep unrolled neural network for real-time MRI-guided brain intervention. Nat Commun. 2023;14(1):8257. https://doi.org/10.1038/s41467-023-43966-w.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yan Q, Liu L, Mei L, Learning unrolling-based neural network for magnetic resonance imaging reconstruction. In: Image analysis and processing—ICIAP, 21st international conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part I Springer. Berlin. 2022;124–36. https://doi.org/10.1007/978-3-031-06427-2_11.

Souza R, Frayne R. A hybrid frequency-domain/image-domain deep network for magnetic resonance image reconstruction. In: 2019 32nd SIBGRAPI conference on graphics, patterns and images (SIBGRAPI); 2019. p. 257–264.https://doi.org/10.1109/SIBGRAPI.2019.00042.

Ye JC, Han Y, Cha E. Deep convolutional framelets: A general deep learning framework for inverse problems. SIAM J Imaging Sci. 2018;11(2):991–1048. https://doi.org/10.1137/17M1141771.

Article  MathSciNet  Google Scholar 

Ali HM. High-Resolution Neuroimaging, In: Halefoğlu AM. edotor, IntechOpen, Rijeka, chap. 7; 2018. https://doi.org/10.5772/intechopen.72427.

Chung H, Ye JC. Score-based diffusion models for accelerated MRI. Med Image Anal. 2022;80: 102479. https://doi.org/10.1016/j.media.2022.102479.

Article  PubMed  Google Scholar 

Xiang T, Yurt M, Syed AB, Setsompop K, Chaudhari A. DDM\(^2\): Self-supervised diffusion MRI denoising with generative diffusion models. In: The eleventh international conference on learning representations; 2023. https://openreview.net/forum?id=0vqjc50HfcC.

Zein ME, Laz WE, Laza M, Wazzan T, Kaakour I, et al. A deep learning framework for denoising MRI images using autoencoders. In: 2023 5th international conference on bio-engineering for smart technologies (BioSMART); 2023. p. 1–4. https://doi.org/10.1109/BioSMART58455.2023.10162068.

Ben Yedder H, Cardoen B, Hamarneh G. Deep learning for biomedical image reconstruction: a survey. Artif Intell Rev. 2021;54(1):215–51. https://doi.org/10.1007/s10462-020-09861-2.

Article  Google Scholar 

Kaur H, Rani J. MRI brain image enhancement using histogram equalization techniques. In: 2016 international conference on wireless communications, signal processing and networking (WiSPNET). 2016. p. 770–773. https://doi.org/10.1109/WiSPNET.2016.7566237.

Kalyani J, Chakraborty M. Contrast enhancement of MRI images using histogram equalization techniques. In: 2020 International conference on computer, electrical & communication engineering (ICCECE); 2020. p. 1–5. https://doi.org/10.1109/ICCECE48148.2020.9223088.

Zimmerman J, Pizer S, Staab E, Perry J, McCartney W, et al. An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement. IEEE Trans Med Imaging. 1988;7(4):304–12. https://doi.org/10.1109/42.14513.

Article  CAS  PubMed  Google Scholar 

Anand S, Shantha R, Selva K. Sharpening enhancement of computed tomography (CT) images using hyperbolic secant square filter. Optik. 2013;124(15):2121–4. https://doi.org/10.1016/j.ijleo.2012.06.026.

Article  Google Scholar 

Wang G, Ye JC, De Man B. Deep learning for tomographic image reconstruction. Nat Mach Intell. 2020;2(12):737–48. https://doi.org/10.1038/s42256-020-00273-z.

Article  Google Scholar 

Sarker IH. Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci. 2021;2(6):420. https://doi.org/10.1007/s42979-021-00815-1.

Article  PubMed  PubMed Central  Google Scholar 

Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929–58.

MathSciNet  Google Scholar 

Livni R. S. Shalev-Shwartz O. Shamir, On the computational efficiency of training neural networks. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger K, editors. Advances in neural information processing systems, vol. 27. Curran Associates Inc; 2014. https://proceedings.neurips.cc/paper_files/paper/2014/file/3a0772443a0739141292a5429b952fe6-Paper.pdf.

Abd-Elmoniem K, Youssef AB, Kadah Y. Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Trans Biomed Eng. 2002;49(9):997–1014. https://doi.org/10.1109/TBME.2002.1028423.

Article  PubMed  Google Scholar 

Burle B, Spieser L, Roger C, Casini L, Hasbroucq T, et al. Spatial and temporal resolutions of EEG: Is it really black and white? a scalp current density view. Int J Psychophysiol. 2015;97(3):210–20. https://doi.org/10.1016/j.ijpsycho.2015.05.004.

Article  PubMed  PubMed Central  Google Scholar 

Shen K, Lu H, Baig S, Wang MR. Improving lateral resolution and image quality of optical coherence tomography by the multi-frame superresolution technique for 3D tissue imaging. Biomed Opt Express. 2017;8(11):4887–918. https://doi.org/10.1364/BOE.8.004887.

Article  PubMed  PubMed Central  Google Scholar 

Bono S, Konishi S. Temperature gradient sensing mechanism using liquid crystal droplets with 0.1-mk-level detection accuracy and high spatial resolution. Sci Rep. 2022;12(1):13733. https://doi.org/10.1038/s41598-022-18008-y.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhang J, Sun K, Yang J, Hu Y, Gu Y, et al. A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis. Commun Eng. 2023;2(1):72. https://doi.org/10.1038/s44172-023-00121-z.

Article  Google Scholar 

Wen Y, Chen L, Deng Y, Zhou C. Rethinking pre-training on medical imaging. J Vis Commun Image Represent. 2021;78: 103145. https://doi.org/10.1016/j.jvcir.2021.103145.

Article  Google Scholar 

Huang SC, Pareek A, Jensen M, Lungren MP, Yeung S, et al. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. NPJ Digit Med. 2023;6(1):74. https://doi.org/10.1038/s41746-023-00811-0.

Article  PubMed  PubMed Central  Google Scholar 

Ahmad W, Ali H, Shah Z, Azmat S. A new generative adversarial network for medical images super resolution. Sci Rep. 2022;12(1):9533. https://doi.org/10.1038/s41598-022-13658-4.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Michailovich O, Tannenbaum A. Despeckling of medical ultrasound images. IEEE Trans Ultrason Ferroelectr Freq Control. 2006;53(1):64–78. https://doi.org/10.1109/TUFFC.2006.1588392.

Article  PubMed  PubMed Central  Google Scholar 

Moinuddin M, Khan S, Alsaggaf AU, Abdulaal MJ, Al-Saggaf UM, et al. Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network. Front Physiol. 2022. https://doi.org/10.3389/fphys.2022.961571.

Article  PubMed  PubMed Central  Google Scholar 

Niyas S, Pawan S, Anand Kumar M, Rajan J. Medical image segmentation with 3d convolutional neural networks: a survey. Neurocomputing. 2022;493:397–413. https://doi.org/10.1016/j.neucom.2022.04.065.

Article  Google Scholar 

de Leeuw ML, den Bouter G, Ippolito TPA, O’Reilly TPA, Remis RF, van Gijzen MB, et al. Deep learning-based single image super-resolution for low-field MR brain images. Sci Rep. 2022;12(1):6362. https://doi.org/10.1038/s41598-022-10298-6.

Article  CAS  Google Scholar 

Huang B, Xiao H, Liu W, Zhang Y, Wu H, et al. MRI super-resolution via realistic downsampling with adversarial learning. Phys Med Biol. 2021;66(20): 205004. https://doi.org/10.1088/1361-6560/ac232e.

Article  Google Scholar 

Jin C, Tanno R, Mertzanidou T, Panagiotaki E, Alexander DC. Learning to downsample for segmentation of ultra-high resolution images. In: International conference on learning representations; 2022. https://openreview.net/forum?id=HndgQudNb91.

Peled S, Yeshurun Y. Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magn Reson Med. 2001;45(1):29–35.

Article  CAS  PubMed  Google Scholar 

Greenspan H, Oz G, Kiryati N, Peled S. MRI inter-slice reconstruction using super-resolution. Magn Reson Imaging. 2002;20(5):437–46. https://doi.org/10.1016/S0730-725X(02)00511-8.

Article  CAS  PubMed  Google Scholar 

Zhai Y, Yao D. A radial-basis function based surface Laplacian estimate for a realistic head model. Brain Topogr. 2004;17(1):55–62. https://doi.org/10.1023/B:BRAT.0000047337.25591.32.

Article  PubMed  Google Scholar 

Rousseau F, Glenn OA, Iordanova B, Rodriguez-Carranza C, Vigneron DB, et al. Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad Radiol. 2006;13(9):1072–81. https://doi.org/10.1016/j.acra.2006.05.003.

Article  PubMed  Google Scholar 

Dey N, Blanc-Feraud L, Zimmer C, Roux P, Kam Z, et al. Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution. Microsc Res Tech. 2006;69(4):260–6. https://doi.org/10.1002/jemt.20294.

Article  PubMed  Google Scholar 

Joshi SH, Marquina A, Osher SJ, Dinov I, Van Horn JD, et al. MRI resolution enhancement using total variation regularization. In: 2009 IEEE international symposium on biomedical imaging: from nano to macro; 2009. p. 161–164. https://doi.org/10.1109/ISBI.2009.5193008.

Akhtar P, Azhar F. A single image interpolation scheme for enhanced super resolution in bio-medical imaging. In: 2010 4th international conference on bioinformatics and biomedical engineering; 2010. p. 1–5. https://doi.org/10.1109/ICBBE.2010.5518164.

Tieng QM, Cowin GJ, Reutens DC, Galloway GJ, Vegh V. MRI resolution enhancement: How useful are shifted images obtained by changing the demodulation frequency? Magn Reson Med. 2011;65(3):664–72. https://doi.org/10.1002/mrm.22653.

Article  PubMed