Texture-driven super-resolution of ultrasound images using optimized deep learning model

References

• Rahman T, Khandakar A, Qiblawey Y, et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med. 2021;132:104319.
   PubMed Web of Science ®Google Scholar
• Saba T, Rehman A, Mehmood Z, et al. Image enhancement and segmentation techniques for detection of knee joint diseases: a survey. Curr Med Imaging. 2018;14(5):704–715.
   Web of Science ®Google Scholar
• Zebari DA, Zeebaree DQ, Abdulazeez AM, et al. Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. Ieee Access. 2020;8:203097–203116.
   Web of Science ®Google Scholar
• Sahiner B, Pezeshk A, Hadjiiski LM, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1):e1–e36.
   PubMed Web of Science ®Google Scholar
• Sitzmann V, Diamond S, Peng Y, et al. End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans Graphic. 2018;37(4):1–13.
   Web of Science ®Google Scholar
• Jacquemet G, Carisey AF, Hamidi H, et al. The cell biologist's guide to super-resolution microscopy. J Cell Sci. 2020;133(11). jcs240713.
   PubMed Web of Science ®Google Scholar
• Sidorov O, Yngve Hardeberg J. Deep hyperspectral prior: single-image denoising, inpainting, super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops; 2019. p. 0–0.
   Google Scholar
• Wang L, Zheng M, Du W, et al. Super-resolution SAR image reconstruction via generative adversarial network. In 2018 12th International Symposium on Antennas, Propagation and EM Theory (ISAPE); IEEE; 2018, December. p. 1–4.
   Google Scholar
• Xia R, Chen Y, Ren B. Improved anti-occlusion object tracking algorithm using unscented rauch-tung-striebel smoother and kernel correlation filter. J King Saud Univ-Comput Inform Sci. 2022.
   Web of Science ®Google Scholar
• Chen Y, Zhang H, Liu L, et al. Research on image inpainting algorithm of improved total variation minimization method. J Ambient Intell Humaniz Comput. 2021: 1–10.
   Web of Science ®Google Scholar
• Chen Y, Liu L, Phonevilay V, et al. Image super-resolution reconstruction based on feature map attention mechanism. Appl Intell. 2021;51(7):4367–4380.
   Web of Science ®Google Scholar
• Chen Y, Phonevilay V, Tao J, et al. The face image super-resolution algorithm based on combined representation learning. Multimed Tools Appl. 2021;80(20):30839–30861.
   Web of Science ®Google Scholar
• Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8:1–74.
   PubMedGoogle Scholar
• Rajput SS, Bohat VK, Arya KV. Grey wolf optimization algorithm for facial image super-resolution. Appl Intell. 2019;49:1324–1338.
   Web of Science ®Google Scholar
• Chen Y, Xia R, Zou K, et al. FFTI: image inpainting algorithm via features fusion and two-steps inpainting. J Vis Commun Image Represent. 2023;91:103776.
   Web of Science ®Google Scholar
• Chen Y, Xia R, Yang K, et al. MFFN: image super-resolution via multi-level features fusion network. Vis Comput. 2023: 1–16.
   PubMed Web of Science ®Google Scholar
• Chen Y, Xia R, Zou K, et al. RNON: image inpainting via repair network and optimization network. Int J Mach Learn Cybern. 2023: 1–17.
   PubMed Web of Science ®Google Scholar
• Moinuddin M, Khan S, Alsaggaf AU, et al. Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network. Front Physiol. 2022: 2326.
   Web of Science ®Google Scholar
• Sawant A, Kulkarni S. Ultrasound image enhancement using super resolution. Biomed Eng Adv. 2022: 100039.
   Google Scholar
• Oktay O, Bai W, Lee M, et al. Multi-input cardiac image super-resolution using convolutional neural networks. In International conference on medical image computing and computer-assisted intervention; Springer, Cham; 2016, October. p. 246–254.
   Google Scholar
• Tanno R, Worrall DE, Ghosh A, et al. Bayesian image quality transfer with CNNs: exploring uncertainty in dMRI super-resolution. In International Conference on Medical Image Computing and Computer-Assisted Intervention; Springer, Cham; 2017, September. p. 611–619.
   Google Scholar
• Liu H, Xu J, Wu Y, et al. Learning deconvolutional deep neural network for high resolution medical image reconstruction. Inf Sci. 2018;468:142–154.
   Web of Science ®Google Scholar
• Zhang Y, An M. Deep learning-and transfer learning-based super resolution reconstruction from single medical image. J Healthc Eng. 2017;2017.
   Web of Science ®Google Scholar
• Zhang S, Liang G, Pan S, et al. A fast medical image super resolution method based on deep learning network. IEEE Access. 2018;7:12319–12327.
   Web of Science ®Google Scholar
• Mahapatra D, Bozorgtabar B, Garnavi R. Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput Med Imaging Graph. 2019;71:30–39.
   PubMed Web of Science ®Google Scholar
• Wang Z, Chen J, Hoi SC. Deep learning for image super-resolution: a survey. IEEE Trans Pattern Anal Mach Intell. 2020.
   Web of Science ®Google Scholar
• Qiu D, Zhang S, Liu Y, et al. Super-resolution reconstruction of knee magnetic resonance imaging based on deep learning. Comput Methods Programs Biomed. 2020;187:105059.
   PubMed Web of Science ®Google Scholar
• Van Sloun RJ, Cohen R, Eldar YC. Deep learning in ultrasound imaging. Proc IEEE. 2019;108(1):11–29.
   Web of Science ®Google Scholar
• Zhang S, Liang G, Pan S, et al. A fast medical image super resolution method based on deep learning network. IEEE Access. 2018;7:12319–12327.
   Web of Science ®Google Scholar
• Mairal J, Koniusz P, Harchaoui Z, et al. Convolutional kernel networks. Adv Neural Inf Process Syst. 2014;27.
   Google Scholar
• Aldosari F, Abualigah L, Almotairi KH. A normal distributed dwarf mongoose optimization algorithm for global optimization and data clustering applications. Symmetry. 2022;14(5):1021.
   Google Scholar
• Sadoun AM, Najjar IR, Alsoruji GS, et al. Utilizing a long short-term memory algorithm modified by dwarf mongoose optimization to predict thermal expansion of Cu-Al2O3 nanocomposites. Mathematics. 2022;10(7):1050.
   Web of Science ®Google Scholar
• Abdel-Nasser M, Melendez J, Moreno A, et al. Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng Appl Artif Intell. 2017;59:84–92.
   Web of Science ®Google Scholar
• Harput S, Christensen-Jeffries K, Ramalli A, et al. 3-D super-resolution ultrasound imaging with a 2-D sparse array. IEEE Trans Ultrason Ferroelectr Freq Control. 2019;67(2):269–277.
   PubMed Web of Science ®Google Scholar
• Zhao N, Wei Q, Basarab A, et al. Single image super-resolution of medical ultrasound images using a fast algorithm. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI); IEEE; 2016, April. p. 473–476.
   Google Scholar
• Baddeley D, Bewersdorf J. Biological insight from super-resolution microscopy: what we can learn from localization-based images. Annu Rev Biochem. 2018;87:965–989.
   PubMed Web of Science ®Google Scholar
• Vandewalle P, Süsstrunk S, Vetterli M. A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP J Adv Signal Pr. 2006;2006:1–14.
   Google Scholar
• Deeba F, et al. Wavelet-based enhanced medical image super resolution. IEEE Access. 2020;8:37035–37044.
   Web of Science ®Google Scholar
• Deeba F, et al. Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm. IET Image Proc. 2020;14(11):2365–2375.
   Web of Science ®Google Scholar
• Deeba F, et al. Multi-scale single image super-resolution with remote-sensing application using transferred wide residual network. Wirel Pers Commun. 2021;120(1):323–342.
   Web of Science ®Google Scholar
• Deeba F, et al. Multimodal-boost: multimodal medical image super-resolution using multi-attention network with wavelet transform. arXiv preprint arXiv:2110.11684, 2021.
   Google Scholar
• Arulkumar V, Aruna M, Lakshmi MA, et al. Super resolution and demosaicing based self learning adaptive dictionary image denoising framework. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS); IEEE; 2021, May p. 1891–1897.
   Google Scholar
• Liu H, Ruan Z, Zhao P, et al. Video super-resolution based on deep learning: a comprehensive survey. Artif Intell Rev. 2022: 1–55.
   Google Scholar
• Afrakhteh S, Jalilian H, Iacca G, et al. Temporal super-resolution of echocardiography using a novel high-precision non-polynomial interpolation. Biomed Signal Process Control. 2022;78:104003.
   Web of Science ®Google Scholar