Henkelman, R.M.: Measurement of signal intensities in the presence of noise in mr images. Medical physics 12(2), 232–233 (1985)

Nowak, R.D.: Wavelet-based rician noise removal for magnetic resonance imaging. IEEE Transactions on Image Processing 8(10), 1408–1419 (1999)

Luisier, F., Blu, T., Wolfe, P.J.: A cure for noisy magnetic resonance images: Chi-square unbiased risk estimation. IEEE Transactions on Image Processing 21(8), 3454–3466 (2012)

You, S., Lei, B., Wang, S., Chui, C.K., Cheung, A.C., Liu, Y., Gan, M., Wu, G., Shen, Y.: Fine perceptive gans for brain mr image super-resolution in wavelet domain. IEEE transactions on neural networks and learning systems (2022)

Hu, S., Lei, B., Wang, S., Wang, Y., Feng, Z., Shen, Y.: Bidirectional mapping generative adversarial networks for brain mr to pet synthesis. IEEE Transactions on Medical Imaging 41(1), 145–157 (2021)

Kumar, A., Welti, D., Ernst, R.R.: Nmr fourier zeugmatography. Journal of Magnetic Resonance (1969) 18(1), 69–83 (1975)

Bhujle, H.V., Vadavadagi, B.H.: Nlm based magnetic resonance image denoising–a review. Biomedical Signal Processing and Control 47, 252–261 (2019)

Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-d magnetic resonance images. IEEE transactions on medical imaging 27(4), 425–441 (2008)

Manjón, J.V., Coupé, P., Martí-Bonmatí, L., Collins, D.L., Robles, M.: Adaptive non-local means denoising of mr images with spatially varying noise levels. Journal of Magnetic Resonance Imaging 31(1), 192–203 (2010)

Breuer, F.A., Kellman, P., Griswold, M.A., Jakob, P.M.: Dynamic autocalibrated parallel imaging using temporal grappa (tgrappa). Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 53(4), 981–985 (2005)

Coupé, P., Manjón, J.V., Robles, M., Collins, D.L.: Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising. IET image Processing 6(5), 558–568 (2012)

Manjón, J.V., Coupé, P., Buades, A., Collins, D.L., Robles, M.: New methods for mri denoising based on sparseness and self-similarity. Medical image analysis 16(1), 18–27 (2012)

Tasdizen, T.: Principal neighborhood dictionaries for nonlocal means image denoising. IEEE Transactions on Image Processing 18(12), 2649–2660 (2009)

Lu, K., He, N., Li, L.: Nonlocal means-based denoising for medical images. Computational and mathematical methods in medicine 2012 (2012)

Gurney-Champion, O.J., Collins, D.J., Wetscherek, A., Rata, M., Klaassen, R., van Laarhoven, H.W., Harrington, K.J., Oelfke, U., Orton, M.R.: Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted mr images. Physics in Medicine & Biology 64(10), 105015 (2019)

Veraart, J., Novikov, D.S., Christiaens, D., Ades-Aron, B., Sijbers, J., Fieremans, E.: Denoising of diffusion mri using random matrix theory. Neuroimage 142, 394–406 (2016)

Wirestam, R., Bibic, A., Lätt, J., Brockstedt, S., Ståhlberg, F.: Denoising of complex mri data by wavelet-domain filtering: Application to high-b-value diffusion-weighted imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 56(5), 1114–1120 (2006)

Golshan, H.M., Hasanzadeh, R.P., Yousefzadeh, S.C.: An mri denoising method using image data redundancy and local snr estimation. Magnetic resonance imaging 31(7), 1206–1217 (2013)

Kala, R., Deepa, P.: Adaptive fuzzy hexagonal bilateral filter for brain mri denoising. Multimedia Tools and Applications, 1–18 (2019)

Zhu, Y., Shen, W., Cheng, F., Jin, C., Cao, G.: Removal of high density gaussian noise in compressed sensing mri reconstruction through modified total variation image denoising method. Heliyon 6(3), 03680 (2020)

Liu, R.W., Shi, L., Huang, W., Xu, J., Yu, S.C.H., Wang, D.: Generalized total variation-based mri rician denoising model with spatially adaptive regularization parameters. Magnetic resonance imaging 32(6), 702–720 (2014)

Wang, Y., Zhou, H.: Total variation wavelet-based medical image denoising. International Journal of Biomedical Imaging 2006 (2006)

Zhang, Y., Yang, Z., Hu, J., Zou, S., Fu, Y.: Mri denoising using low rank prior and sparse gradient prior. IEEE Access 7, 45858–45865 (2019)

Wink, A.M., Roerdink, J.B.: Denoising functional mr images: a comparison of wavelet denoising and gaussian smoothing. IEEE transactions on medical imaging 23(3), 374–387 (2004)

Simi, V., Edla, D.R., Joseph, J., Kuppili, V.: Analysis of controversies in the formulation and evaluation of restoration algorithms for mr images. Expert Systems with Applications 135, 39–59 (2019)

Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on mri. Zeitschrift für Medizinische Physik 29(2), 102–127 (2019)

Benou, A., Veksler, R., Friedman, A., Raviv, T.R.: Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced mri sequences. Medical image analysis 42, 145–159 (2017)

Jiang, D., Dou, W., Vosters, L., Xu, X., Sun, Y., Tan, T.: Denoising of 3d magnetic resonance images with multi-channel residual learning of convolutional neural network. Japanese journal of radiology 36(9), 566–574 (2018)

You, X., Cao, N., Lu, H., Mao, M., Wanga, W.: Denoising of mr images with rician noise using a wider neural network and noise range division. Magnetic Resonance Imaging 64, 154–159 (2019)

Panda, A., Naskar, R., Rajbans, S., Pal, S.: A 3d wide residual network with perceptual loss for brain mri image denoising. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7 (2019). IEEE

Ran, M., Hu, J., Chen, Y., Chen, H., Sun, H., Zhou, J., Zhang, Y.: Denoising of 3d magnetic resonance images using a residual encoder–decoder wasserstein generative adversarial network. Medical image analysis 55, 165–180 (2019)

Aetesam, H., Maji, S.K.: Attention-based noise prior network for magnetic resonance image denoising. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–4 (2022). IEEE

Bermudez, C., Plassard, A.J., Davis, L.T., Newton, A.T., Resnick, S.M., Landman, B.A.: Learning implicit brain mri manifolds with deep learning. In: Medical Imaging 2018: Image Processing, vol. 10574, p. 105741 (2018). International Society for Optics and Photonics

Moreno López, M., Frederick, J.M., Ventura, J.: Evaluation of mri denoising methods using unsupervised learning. Frontiers in Artificial Intelligence 4, 75 (2021)

Isa, I.S., Sulaiman, S.N., Mustapha, M., Darus, S.: Evaluating denoising performances of fundamental filters for t2-weighted mri images. Procedia Computer Science 60, 760–768 (2015)

Aetesam, H., Maji, S.K.: L2- l1 fidelity based elastic net regularisation for magnetic resonance image denoising. In: 2020 International Conference on Contemporary Computing and Applications (IC3A), pp. 137–142 (2020). IEEE

Shlykov, V., Kotovskyi, V., Višniakov, N., Šešok, A.: Model for elimination of mixed noise from mri heart images. Applied Sciences 10(14), 4747 (2020)

Toprak, A., Güler, I.: Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter. Digital signal processing 17(4), 711–723 (2007)

Toprak, A., Özerdem, M.S., Güler, İ.: Suppression of impulse noise in mr images using artificial intelligent based neuro-fuzzy adaptive median filter. Digital signal processing 18(3), 391–405 (2008)

Lin, L., Meng, X., Liang, X.: Reduction of impulse noise in mri images using block-based adaptive median filter. In: 2013 IEEE International Conference on Medical Imaging Physics and Engineering, pp. 132–134 (2013). IEEE

Mafi, M., Martin, H., Adjouadi, M.: High impulse noise intensity removal in mri images. In: 2017 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1–6 (2017). IEEE

HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Soroushmehr, S., Shirani, S., Samavi, S., Najarian, K.: Real-time impulse noise removal from mr images for radiosurgery applications. arXiv preprint arXiv:1707.05975 (2017)

HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Soroushmehr, R., Shirani, S., Najarian, K., Samavi, S.: Adaptive real-time removal of impulse noise in medical images. Journal of medical systems 42(11), 216 (2018)

Chanu, P.R., Singh, K.M.: Impulse noise removal from medical images by two stage quaternion vector median filter. Journal of medical systems 42(10), 197 (2018)

Sheela, C.J.J., Suganthi, G.: An efficient denoising of impulse noise from mri using adaptive switching modified decision based unsymmetric trimmed median filter. Biomedical Signal Processing and Control 55, 101657 (2020)

HosseinKhani, Z., Karimi, N., Soroushmehr, S.M.R., Hajabdollahi, M., Samavi, S., Ward, K., Najarian, K.: Real-time removal of random value impulse noise in medical images. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3916–3921 (2016). IEEE

HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Najarian, K., Emami, A., Shirani, S., Samavi, S., Soroushmehr, S.M.R.: Real-time removal of impulse noise from mr images for radiosurgery applications. International Journal of Circuit Theory and Applications 47(3), 406–426 (2019)

Jiang, J., Zhang, L., Yang, J.: Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE transactions on image processing 23(6), 2651–2662 (2014)

Huang, T., Dong, W., Xie, X., Shi, G., Bai, X.: Mixed noise removal via laplacian scale mixture modeling and nonlocal low-rank approximation. IEEE Transactions on Image Processing 26(7), 3171–3186 (2017)

Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)

Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)

Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)

Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Transactions on computational imaging 3(1), 47–57 (2016)

Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694–711 (2016). Springer

Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004)

Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. IEEE Transactions on Image Processing 21(4), 1488–1499 (2011)

Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015). PMLR

Ioffe, S.: Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. In: Advances in Neural Information Processing Systems, pp. 1945–1953 (2017)

Ye, R., Liu, F., Zhang, L.: 3d depthwise convolution: Reducing model parameters in 3d vision tasks. In: Canadian Conference on Artificial Intelligence, pp. 186–199 (2019). Springer

Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. arXiv preprint arXiv:1903.10082 (2019)

Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Transactions on Image Processing 26(7), 3142–3155 (2017)

Cocosco, C.A., Kollokian, V., Kwan, R.K.-S., Pike, G.B., Evans, A.C.: Brainweb: Online interface to a 3d mri simulated brain database. In: NeuroImage (1997). Citeseer

Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: 2011 International Conference on Computer Vision, pp. 479–486 (2011). IEEE

Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9446–9454 (2018)

Aetesam, H., Maji, S.K.: Noise dependent training for deep parallel ensemble denoising in magnetic resonance images. Biomedical Signal Processing and Control 66, 102405 (2021)