Derin Öğrenme ile Görüntülerde Gürültü Azaltma Üzerine Kapsamlı Bir İnceleme

Year 2022, Volume: 34 Issue: 1, 65 - 90, 30.03.2022

Ahmet Yapıcı , M. Ali Akcayol

https://doi.org/10.7240/jeps.938188

Cited By: 1

Abstract

Günlük hayatımızda ve bilimsel araştırmalarda gerçeğe yakın ve gürültüsüz görüntülere olan ihtiyaç artmaktadır. Ancak görüntüler, gürültü ile bozulmakta ve bu da görsel görüntü kalitesinin düşmesine neden olmaktadır. Bu nedenle, görüntü özelliklerini kaybetmeden gürültüyü azaltmak için çalışmalar yapılmaktadır. Şimdiye kadar, gürültüyü azaltmak için çeşitli yöntemler önerilmiş olup, her yöntemin farklı avantajları bulunmaktadır. Bu makalede, alanında en iyi sonucu elde eden yöntemler hakkında bilgi verilerek, video ve sabit görüntülerinde gürültü azaltma alanında yapılan geleneksel gürültü giderme yöntemleri ve derin öğrenme yöntemlerine ait çalışmalar özetlenip, elde edilen sonuçlar birbirleriyle karşılaştırılmaktadır. Yapılan araştırmalar deneylerin toplamsal beyaz Gauss gürültüsü durumuna odaklandığını göstermektedir. Görüntülerde gürültü giderme aşamasında zaman içerisinde geleneksel gürültü giderme yöntemleri, makine öğrenmesi yöntemleri, derin öğrenme yöntemleri ve diğer matematiksel yöntemler kullanılmış olup, derin öğrenme yöntemleri daha başarılı sonuçlar elde etmektedir. Ancak elde edilen verilere göre orijinal görüntü çiftlerine sahip olmadan modelin eğitilmesi konusunda çalışmaların yetersiz olduğu ve değişik gürültü seviyelerinde tek bir yöntemin başarılı olamadığı görülmüştür. İleride yapılacak çalışmalarda gerçek hayattaki görüntülerde var olan gürültülerin nasıl giderileceği konusuna daha detaylı odaklanılması gerektiği görülmektedir.

Keywords

Görüntülerde Gürültü Giderme, Görüntü restorasyonu, Video üzerinde gürültü azaltma, Derin öğrenme tabanlı gürültü giderme

A Comprehensive Review of Image Denoising With Deep Learning

Abstract

In daily life and scientific searches, the need for real-like and denoised images is increasing. But images are distorted by noise, resulting in lower visual image quality. For this reason, noise removal studies are carried out on images to increase the quality. Until now, various methods have been proposed to decrease noise, each technique has different advantages. This paper gives information about the methods that achieve the best results in their field and summarizes the studies about traditional denoising and deep learning based denoising methods in the field of noise reduction in video and images and compares the studies with each other. Researches show that experiments focus on the case of additive white Gaussian noise. Traditional noise removal methods, machine learning methods, deep learning methods and other mathematical methods have been used in image denoising problem over time, and deep learning methods achieve more successful results. However, according to the obtained data, it has been seen that the studies on training the model without having the original image pairs were insufficient and a single method could not be successful at different noise levels. In future studies, it is necessary to focus on how to remove the noise in real-life images.

Keywords

Image denoising, Image restoration, Video denoising, Deep learning-based noise reduction

References

• Abiri, N., Linse, B., Edén, P., Ohlsson, M. (2019). Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems, Elsevier, 137-146.
• Zhou, C., Paenroth, R. (2017). Anomaly Detection with Robust Deep Autoencoders, ACM KDD, 13-17 Ağustos, Halifax, NS, Canada, 665-674.
• Wen, B., Ravishankar, S., Bresler, Y. (2017). VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising, IEEE Transactions on Image Processing, 28(4), 1691-1704.
• Chang, Y. (2019). Research on demotion blur image processing based on deep learning, J. Vis. Commun. Image R. Elsevier, 60, 371–379.
• Erol A., Gürbüz M., Gangal, A. (2016). Video Görüntülerindeki Periyodik Gürültülerin Yok Edilmesi, Researchgate, 535-537.
• Gondara, L. (2016). Medical image denoising using convolutional denoising autoencoders, IEEE 16th International Conference on Data Mining Workshops, 12-15 Aralık, Barselona, 241-246.
• Hawwar, Y., Reza, A. (2002). Spatially adaptive multiplicative noise image denoising technique, IEEE Transactions on Image Processing, 11(12), 1397-1404.
• Bioucas-Dias, J. M., Figueiredo, M. A. T., (2010). Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization, IEEE Transactions on Image Processing, 19(7), 1720-1730.
• Bovik, A. (2000). Handbook of Image and Video Processing, Academic Press, Kanada, 330-331.
• Leysa, C., Ley, C., Klein, O., Bernard, P., Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median, Journal of Experimental Social Psychology, 49(4), 764-766.
• Shin, DH., Park RH., Yang S., Jung JH. (2005). Block-based noise estimation using adaptive gaussian filtering, IEEE Trans Consum Electron, 51(1), 218–226.
• Jolliffe, I. (2005). Principal component analysis, 2. Baskı. New York, Springer-Verlag.
• Horé, A., Ziou, D. (2010). Image quality metrics: PSNR vs. SSIM, 20th International Conference on Pattern Recognition, 2366-2369.
• Liu, D., Li, D., Song, H. (2016). Image Quality Assessment Using Regularity of Color Distribution, IEEE Access, vol. 4, 4478-4483.
• Pratt, W. K., (2007). Digital Image Processing, Fourth Edition, John Wiley & Sons Inc.
• Proakis, J., Salehi, M. (2002). Communication Systems Engineering, Second Ed., Prentice-Hall, Upper Saddle River, 217-267.
• Paonessa, S. (2021). Reducing Signal Noise in Practice. https://www.predig.com/whitepaper/reducing-signal-noise-practice (21.01.2021).
• Kong, X. (2003). Removing Periodic Noise in F-wave: A Dynamic Notch Filter Approach, Proceedings of the 25th Annual International Conference of the IEEE EMBS, 17-21 Eylül, Cancun, Meksika, 2503-2506.
• Diwakar, M., Kumar, M. (2018). A review on CT image noise and its denoising, Biomed Signal Process Control, Nisan, Dehradun, 73–88.
• Weizheng, X., Chenqi, X., Zhengru, J., Yueping, H. (2020). Digital Image Denoising Method Based on Mean Filter, International Conference on Computer Engineering and Application (ICCEA), 857-859.
• Tania, S., Rowaida, R. (2016). A Comparative Study of Various Image Filtering Techniques for Removing Various Noisy Pixels in Aerial Image, International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(3), 113-124.
• Pratt, W. K. (1972). Generalized Wiener Filtering Computation Techniques, IEEE Transactions on Computers, C-21(7), 636-641.
• Lee, Y., Kassam, S. (1985). Generalized median filtering and related nonlinear filtering techniques, IEEE Transactions on Acoustics, Speech, and Signal Processing, 33(3), 672-683.
• Yin, L., Yang, R., Gabbouj, M., Neuvo, Y. (1996). Weighted median filters: a tutorial, IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing, 43(3), 157-192.
• Tomasi, C., Manduchi R. (1998). Bilateral filtering for gray and color images, Sixth international conference on computer vision IEEE, 7 Ocak, Bombay, India, 839–846.
• Estrela V. V., Magalhães, H. A., Saotome, O. (2016). Total Variation Applications in Computer Vision, arXiv:1603.09599.
• Rudin, L., Osher, S., Fatemi, E. (1992). Nonlinear Total Variation based Noise Removal Algorithms, Physica D, vol 60, 259–268.
• Esedoglu, S., Osher, S. (2004). Decomposition of Images by the Anisotropic Rudin-Osher-Fatemi Model. Communications on Pure and Applied Mathematics, vol 57, 1609–1626.
• Chambolle, A., Caselles, V., Novaga, M., Cremers, D., Pock, T. (2009). An introduction to Total Variation for Image Analysis, Radon Series Comp. Appl. Math, 9, 263–340.
• Chan, T. F., Esedoglu, S., Park, F., Yip, A. (2004). Recent developments in total variation image restoration, Mathematical Models of Computer Vision, CAM Report 05-01, Department of Mathematics, UCLA.
• Buades, A., Coll, B., Morel, J. M. (2005). A non-local algorithm for image denoising, IEEE computer society conference on computer vision and pattern recognition, San Diego, 2(2), 60–65.
• Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering, IEEE Transactions on Image Processing, 16(8), 2080-2095.
• Mäkinen, Y., Azzari, L., Monge, E., Maggioni, M., Danielyan, A., Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K. (2019). Image and video denoising by sparse 3D transform-domain collaborative filtering. https://www.cs.tut.fi/~foi/3D-DFT/ (21.02.2021).
• Dabov. K., Foi, A., Katkovnik, V., Egiazarian, K. (2009). BM3D image denoising with shape-adaptive principal component analysis, Signal processing with adaptive sparse structured representations, SPARS'09- Signal Processing with Adaptive Sparse Structured Representations, Saint Malo, France, DOI: 10.1109/TIP.2018.2865684.
• Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K. (2006). Image denoising with block-matching and 3D filtering, Proceedings of SPIE, 6064, 354-365.
• Makkar, H., Lamba, O. (2017). An Improved VBM3D Filtering Technique for Removal Noise in Video Signals, European Journal of Advances in Engineering and Technology, ResearchGate, 4(8), 584-591.
• Maggioni, M., Boracchi. G., Foi A., Egiazarian, K. (2012). Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms, IEEE Transactions on Image Processing, 21(9), 3952–3966.
• Khawase, S., Kamble, S., Thakur, N., Patharkar, A. (2017). An Overview of Block Matching Algorithms for Motion Vector Estimation, Intelligent and Computing in Engineering, Vol. 10 ISSN 2300-5963c, 217–222.
• Delon, J., Houdard, A. (2018). Gaussian Priors for Image denoising, Bertalmío, Marcelo, Denoising of Photographic Images and Video Fundamentals, Open Challenges and New Trends, Advances in Computer Vision and Pattern Recognition book series (ACVPR), Springer.
• Russel, S., Norvig, P. (2010). Artificial Intelligence A Modern Approach, Third Edition, Pearson Education.
• Wright, J., Yang A. Y., Ganesh A., Sastry S. S., Ma, Y. (2009). Robust face recognition via sparse representation, IEEE Transactions on Pattern analysis and Machine Intelligence, 31(2), 210-227.
• Deng, W., Hu, J., Guo, J. (2012). Extended SRC: Undersampled face recognition via intraclass variant dictionary, IEEE Trans. Pattern Anal. Mach. Intell, 34(9), 1864-1870.
• Zhang, L., Zhou, W. (2009). On the sparseness of 1-norm support vector machines, Neural Networks, 23(3), 373-385.
• Gao, S., Tsang, I.W.H., Chia, L. T. (2010). Kernel sparse representation for image classification and face recognition, 11th Eur. Conf. Comput. Vis., 6314, 1-14.
• Xu, S., Yang, X., Jiang, S. (2017). A fast nonlocally centralized sparse representation algorithm for image denoising, Signal Processing, Volume 131, 99-112.
• Ji, H., Liu, C. Q., Shen, ZW., Xu, Y. H. (2010). Robust video denoising using low rank matrix completion, IEEE computer vision and pattern recognition, San Francisco, 1791–1798.
• Gu, S. H., Xie, Q, Meng, D. Y, Zuo, W. M, Feng, X. C., Zhang, L. (2017). Weighted nuclear norm minimization and its applications to low level vision, Int J Comput Vis, 121(2), 183–208.
• Ji, H., Huang, S. B., Shen, Z. W., Xu, Y. H. (2011). Robust video restoration by joint sparse and low rank matrix approximation, SIAM J Imaging Sci, 4(4), 1122–1142.
• Fan, L., Zhang, F., Fan, H. (2019). Brief review of image denoising techniques, Visual Computing for Industry, Biomedicine, and Art, 2(7).
• Şeker, Ş. (2008). Tekil Değer Ayrışımı. http://bilgisayarkavramlari.com/2008/12/29/tekil-deger-ayrisimi-singular-value-decomposition/, (21.01.2021).
• Hornick, M. (2016). Using SVD for Dimensionality Reduction. https://blogs.oracle.com/r/using-svd-for-dimensionality-reduction, (01.02.2021).
• Aharon, M., Elad, M., Bruckstein, A. (2006). K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Transactions on Signal Processing, 54(11), 4311-4322.
• Gu, S., Zhang, L., Zuo, W. Feng, X. (2014). Weighted nuclear norm minimization with application to image denoising, 2014 IEEE conference on computer vision and pattern recognition, Columbus, 2862–2869.
• Mallat, S. (1998). A Wavelet Tour of Signal Processing, Academic Press, San Diego.
• Bhuiyan, M., Ahmad, M., Swamy, M. (2008). Wavelet-based image denoising with the normal inverse Gaussian prior and linear MMSE estimator, Iet Image Processing, volume 2, 203-217.
• Hyvarinen, A., Oja, E., Hoyer, P., Hurri, J. (1998). Image feature extraction by sparse coding and independent component analysis, IEEE 14th international conference on pattern recognition, Brisbane, 1268–1273.
• Nigam, V., Luthra, S., Bhatnagar, S. (2010). A Comparative Study of Thresholding Techniques for Image Denoising, International Conference on Computer and Communication Technology, Allahabad, 173-176.
• Donoho, D. L., Johnstone, I. M., (1995). Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association, 90(432), 1200-1224.
• Donoho, D. L., (1995). Denoising by soft-thresholding, IEEE Trans. Information Theory, 41(3), 613-627.
• Biswas, M., Om, H. (2021). A New Soft-Thresholding Image Denoising Method, 2nd International Conference on Communication, Computing & Security [ICCCS-2012], 6, 10-15.
• Chang. S. G., Yu, B., Vetterli, M. (2000). Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing, 9(9), 1532-1546.
• Starck, J., Fadili, J., Murtagh, F. (2007). The Undecimated Wavelet Decomposition and its Reconstruction, IEEE Transactions On Image Processing, 16(2).
• Starck, J. L., Elad, M., Donoho, D. L., (2004). Redundant multiscale transforms and their application for morphological component analysis, Adv. Imag. Electron Physics, 287-348.
• Barbero, M., Hofmann, H., Wells, N. (1991). DCT source coding and current implementations for HDTV, EBU Technical Review.
• Lukin, V. V., Fevralev, D. V., Nikolay, N., Abramov, S. (2010). Astola Discrete cosine transform-based local adaptive filtering of images corrupted by nonstationary noise, Journal of Electronic Imaging, 19(2).
• Kim, J., Lee, J. K., Lee, KM. (2016). Accurate image super-resolution using very deep convolutional networks, IEEE conference on computer vision and pattern recognition, 27-30 Haziran, Las Vegas, 1646–1654.
• Wang, X., Chan, K., Yu, K., Dong, C., Loy, C. C. (2019). EDVR: Video restoration with enhanced deformable convolutional networks, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 1954-1963.
• Lefkimmiatis, S. (2018). Universal denoising networks: A novel cnn architecture for image denoising, CVPR, 3204–3213.
• Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang L. (2017). Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising, IEEE Transactions on Image Processing, 26(7), 3142-3155.
• Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., Aila, T. (2018). Noise2Noise: Learning image restoration without clean data, ICML, volume 80, 2965–2974.
• Batson, J., Royer, L. (2019). Noise2self: Blind denoising by self-supervision, arXiv:1901.11365.
• Krull, A., Buchholz, T., Jug, F. (2019). Noise2void - learning denoising from single noisy images, CVPR, arXiv:1811.10980.
• Alimovskia, E., Erdemira, G. (2021). Veri Artırma Tekniklerinin Derin Öğrenmeye Dayalı Yüz Tanıma Sisteminde Etkisi, İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 3(1): 76-80.
• Fabio, P., Christina, V., Sandra, A., Eduardo, V. (2018). Data augmentation for skin lesion analysis, ISIC skin image analysis workshop and challenge, MICCAI.
• Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database, IEEE Computer Vision and Pattern Recognition (CVPR).
• LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. (1998). Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), 2278–2324.
• Szandala, T., (2020). Review and comparison of commonly used activation functions for deep neural networks, Bio-inspired Neurocomputing, 903, 203–224.
• Ahn, H., Rhee, S. B. (2015). Research of Object Recognition and Tracking Based on Feature Matching, Park J., Stojmenovic I., Jeong H., Yi G. (eds) Computer Science and its Applications, Lecture Notes in Electrical Engineering, vol 330, Springer, Berlin, Heidelberg, 1071-1076
• Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks, Advances, Neural Information Processing Systems, Kasım 2012, NY United States, 1097–1105.
• Nair, V., Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines, 27th international conference on machine learning, Haziran 2010, Haifa, Israel, 807–814.
• Simonyan, K., Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556.
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2015). Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 1-9.
• Lin, M., Chen, Q., Yan, S. (2013). Network in network, Neural Computing and Applications, Journal of Computer and Communications, 8(11), arXiv:1312.4400.
• Chunwei, T., Lunke, F., Wenxian, Z., Yong, X., Wangmeng, Z., Chia-Wen L. (2020). Deep Learning on Image Denoising: An Overview, Neural networks: the official journal of the International Neural Network Society, 251-275. arXiv:1912.13171.
• He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 27-30 Haziran, Las Vegas, NV, USA, 770–778.
• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. (2014). Generative Adversarial Networks, Advances in Neural Information Processing Systems, 3(11), arXiv:1406.2661.
• Tran, L., Yin, X., Liu, X. (2017). Disentangled representation learning gan for pose-invariant face recognition, IEEE Conference on Computer Vision and Pattern Recognition, 1415–1424.
• Li, Y., Li, Z., Kejun, K. (2013). A novel image restoration method assisted by reference image in dual-energy CT, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference, 1-4, DOI: 10.1109/NSSMIC.2013.6829279.
• Wu, Y., Zhao, W., Zhang, Q. (2013). The improvement of audio noise reduction system based on LMS algorithm, 2013 International Conference on Computer Sciences and Applications, 590-594.
• LeCun, Y. Bengio, Y., Hinton, G. (2015). Deep learning, Nature, 521(7553), 436-444.
• Ha, V. K., Ren, J., Xu, X., Zhao, S., Xie, G., Vargas, V.M. (2018). Deep Learning Based Single Image Super-Resolution: A Survey, Advances in Brain Inspired Cognitive Systems BICS 2018, Springer, vol 10989.
• Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L. (2019). Toward convolutional blind denoising of real photographs, IEEE Conference on Computer Vision and Pattern Recognition, 16-20 Haziran, CA, USA, 1712–1722.
• Liu, H., Ruan, Z., Zhao, P., Dong, C., Shang, F., Liu, Y., Yang, L. (2020). Video Super Resolution Based on Deep Learning: A Comprehensive Survey, Computer Science, Computer Vision and Pattern Recognition, arXiv:2007.12928.
• Farooque, M. A., Rohankar, J. S. (2013). Survey on various noises and techniques for denoising the color image, International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2(11), 217–221.
• Tian, C., Xu, Y., Fei, L., Yan, K. (2019). Deep Learning for Image Denoising: A Survey, ICGEC 2018 Advances in Intelligent Systems and Computing, Singapore, vol 834.
• Jeon, W., Jeong, W., Son, K., Yang, H. (2018). Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks, Optics Letters, 43(17), 4240–4243.
• Liu, S., Deng, W. (2015). Very deep convolutional neural network based image classification using small training sample size, IAPR Asian Conference on Pattern Recognition (ACPR), 730-734.
• Yu, F., Koltun, V., Funkhouser, T. (2017). Dilated residual networks, Computer Vision and Pattern Recognition (CVPR) (2017). arXiv: 1705.09914.
• Chen, C., Xiong, Z., Tian, X., Wu, F. (2018). Deep boosting for image denoising, European Conference on Computer Vision (ECCV), 4-8 Eylül, Munich, Germany, 3–18.
• Tai, Y., Yang J., Liu, X. (2017). MemNet: a persistent memory network for image restoration, IEEE Int. Conf. on Computer Vision (ICCV 2017), Venice, Italy, 4539– 4547.
• Liu, P., Zhang, H., Zhang, K., Lin, L., Zuo, W. (2018). Multi-level wavelet-cnn for image restoration, IEEE Conference on Computer Vision and Pattern Recognition Workshops, 18-22 Haziran, Salt Lake City, Utah, 773–782.
• Zhao, Y. Q., Yang, J. (2015). Hyperspectral image denoising via sparse representation and low-rank constraint, IEEE Transactions on Geoscience and Remote Sensing, 53(1), 296-308.
• Bui A. T., Im, J. K., Apley, D. W., Runger G. C. (2019). Projection-free kernel principal component analysis for denoising, Elsevier Neurocomputing, 357, 163-176.
• Cha, S., Park, T., Kim, B., Baek, J., Moon, T. (2021). GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images, International Conference on Learning Representations (ICLR).
• Luo, Z., Chen, S., Qian, Y. (2019). A Deep Optimization Approach for Image Deconvolution, Computer Vision and Pattern Recognition, arXiv:1904.07516.
• Wang, T., Sun, M., Hu, K. (2018). Dilated deep residual network for image denoising, International Conference on Tools with Artificial Intelligence, ICTAI, 6-8 Kasım, Boston, 1272-1279.
• Chen, Y., Pock., T. (2016). Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1256-1272.
• Lai, W. S., Huang J. B., Ahuja, N., Yang M. H. (2017). Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution, 30th IEEE Conference on Computer Vision and Pattern Recognition, 21-26 Haziran, Honolulu, 5835-5843.
• Tian, C., Xu, Y., Zuo, W. (2020). Image denoising using deep cnn with batch renormalization, Neural Networks, 121, 461–473.
• Anwar, S., Huynh, C. P., Porikli, F. (2017). Chaining identity mapping modules for image denoising, arXiv:1712.02933.
• Anandbabu, G., Merajothu, N., Kirankumar, G. (2018). Convolutional neural network based image denoising for better quality of images. International Journal of Engineering and Technology(UAE), 7, 356-361.
• Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A., Bishop, R., Rueckert, D., Wang, Z. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, IEEE conference on computer vision and pattern recognition, 27-30 Haziran, Las Vegas, 1874-1883.
• Tian, C., Xu, Y., Fei, L., Wang, J., Wen, J., Luo, N. (2019). Enhanced cnn for image denoising, CAAI Transactions on Intelligence Technology, 4(1), 17–23.
• Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., Liu, H. (2020). Attention-guided cnn for image denoising, Neural Networks, 124, 117-129.
• Zoran, D., Weiss, Y. (2011). From learning models of natural image patches to whole image restoration, IEEE International Conference on Computer Vision, 6-13 Kasım, Barcelona, 479–486.
• Schmidt, U., Roth, S. (2014). Shrinkage fields for effective image restoration, IEEE Conference on Computer Vision and Pattern Recognition, 24-27 Haziran, Columbus, 2774–2781.
• Aljadaany, R., Pal, D. K., Savvides, M. (2019). Proximal splitting networks for image restoration, International Conference on Image Analysis and Recognition, Springer, 3-17.
• Zhang, K., Zuo, W., Gu, S., Zhang, L. (2017). Learning deep cnn denoiser prior for image restoration, IEEE Conference on Computer Vision and Pattern Recognition, 21-27 Haziran, Honolulu, 3929–3938.
• Bengio, Y. (2009). Learning deep architectures for AI, Foundations and Trends in Machine Learning, 2(1), 1–127.
• Patilab, S., Naik, G., Pai, R., Gad, R. (2018). Stacked Autoencoder for classification of glioma grade III and grade IV, Biomedical Signal Processing and Control, Elsevier, 46, 67-75.
• Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P. (2008). Extracting and Composing Robust Features with Denoising Autoencoders, 25th International Conference on Machine Learning, Haziran, Helsinki, Finland, 1096–1103.
• Chinner, H. (2015). Denoising AutoEncoders, http://www.rubylab.io/2015/04/28/denoising-autoencoder-tutorial/, (01.11.2020).
• Harish, H. (2019). Denoising AutoEncoders, https://medium.com/@harishr2301/denoising-autoencoders-996e866e5cd0, (01.11.2020).
• Liu, G., Bao, H., Han, B. (2018). A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis, Mathematical Problems in Engineering, DOI: 10.1155/2018/5105709.
• Jozdani, S., Johnson, B., Chen, D. (2019). Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification, Remote Sens, 11(14), 1713s.
• Ng, A. (2018), Sparse autoencoder, CS294A Lecture notes, https://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf, (07.04.2021).
• Kingma, D., Welling, M. (2019). An Introduction to Variational Autoencoders, Foundations and Trends in Machine Learning, 12(2019), 307-392.
• Im D., Im, J., Ahn, S., Memisevic, R., Bengio, Y. (2017), Denoising Criterion for Variational Auto-Encoding Framework, AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence, 2059-2065.
• Creswell, A., Bharath, A. A. (2018). Denoising Adversarial Autoencoders, arXiv:1703.01220v4.
• Chena, X., Songa, L., Yanga, X. (2016). Deep RNNs for Video Denoising, Applications of Digital Image Processing, SPIE vol 9971.
• Antczak, K. (2018). Deep Recurrent Neural Networks for ECG Signal Denoising, 9(1), arXiv:1807.11551, 135s.
• Rajeev, R., Samath, J., Karthikeyan, N. (2019). An Intelligent Recurrent Neural Network with Long Short-Term Memory (LSTM) BASED Batch Normalization for Medical Image Denoising, Journal of Medical Systems, Springer Science, 43(8), 234s.
• Cho, K. (2013). Boltzmann Machines for Image Denoising, Artificial Neural Networks and Machine Learning – ICANN, Springer, 10-13 Eylül, Sofia, 611-618.
• Hinton, G. E., Salakhutdinov. R. R. (2006). Reducing the Dimensionality of Data with Neural Networks, Science, 313(5786), 504–507.
• Keyvanrad, M., Pezeshki, M., Homayounpour, M. (2013), Deep Belief Networks for Image Denoising, arXiv:1312.6158.
• Wang, C., Zhou, S. K., Cheng, Z., (2020). First image then video: A two-stage network for spatiotemporal video denoising, arXiv:2001.00346.
• Sheeba, M. C., Seldev C.D. C. (2019). A review on video denoising methods, 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC), 1-6.
• Arias, P., Morel, J. M. (2018). Video Denoising via Empirical Bayesian Estimation of Space-Time Patches, Journal of Mathematical Imaging and Vision, 60(1). 70–93.
• Buades, A., Lisani, J. (2016). Patch-Based Video Denoising With Optical Flow Estimation, IEEE Trans. IP, 25(6), 2573–2586.
• Tassano, M., Delon, J., Veit, T. (2019). Dvdnet: A fast network for deep video denoising, International Conference on Image Processing (ICIP), IEEE, 22-25 Eylül, Taipei, 1805–1809.
• Paramkusam, A., Reddy, V. S. K. (2014). Two-layer motion estimation algorithm for video coding, Electronics Letters, vol 50, 276-278.
• Chen, H., Jin, Y., Xu, K., Chen, Y., Zhu, C. (2021). Multiframe-to-Multiframe Network for Video Denoising, IEEE Transactions on Multimedia, DOI: 10.1109/TMM.2021.3077140.
• Zhang, X., Yang, Y., Lin, L. (2018). Edge-aware image denoising algorithm, Journal of Algorithms & Computational Technology, Volume 13, 1–10.
• Rudin, L. I., Osher, S., Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms, 11th annual international conference of the center for nonlinear studies on experimental mathematics: computational issues in nonlinear science, Elsevier North-Holland, Inc, New York, 259–268.
• Sutour, C., Deledalle, C. A., Aujol, J. F. (2014). Adaptive regularization of the nl-means: application to image and video denoising, IEEE Trans Image Process, 23(8), 3506–3521.
• Buades, A., Coll, B., Morel, J. M. (2005). A non-local algorithm for image denoising, 2005 IEEE computer society conference on computer vision and pattern recognition, IEEE, San Diego, 60–65.
• Guo. Q., Zhang, C. M., Zhang, Y. F., Liu, H. (2016). An efficient SVD-based method for image denoising, IEEE Transactions On Circuits And Systems For Video Technology, 26(5), 868–880.
• Dong, W. S., Zhang, L., Shi, G. M., Li, X. (2013). Nonlocally centralized sparse representation for image restoration, IEEE Trans Image Process, 22(4), 1620–1630.
• Yeşiloğlu, A., Ekşioğlu, E. M. (2016). Seyrek İşaret İşlemede Sınıflandırma Uygulamaları ve Çekirdek Tabanlı Yaklaşımlar, 24.Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Zonguldak, 1157-1160.
• Erdogan, H. T., Erdem, E., Erdem, A. (2013). Alan Kovaryansları İçin Grup Seyrekliğine Dayalı Seyrek Kodlama, 21. IEEE Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Girne, K.K.T.C., 1-3.
• Andrearczyk, V., Whelan, P. F. (2016). Using Filter Banks in Convolutional Neural Networks for Texture Classification, arXiv:1601.02919.
• Eslahi, N., Aghagolzadeh, Q. (2016). Compressive sensing image restoration using adaptive curvelet thresholding and nonlocal sparse regularization, IEEE Trans. Image Process., 25(7), 3126-3140.
• Lucas, A., Iliadis, M., Molina, R., Katsaggelos, A. K. (2018). Using deep neural networks for inverse in imaging: beyond analytical methods, IEEE Signal Processing Magazine, Ocak 2018, 35(1), 20-36.
• Zhang, Y., Xiao, J., Peng, J., Zong, X. (2018). Kernel Wiener Filtering Model with Low-Rank Approximation for Image Denoising, Information Sciences.
• Jain, V., Seung, H. (2009). Natural Image Denoising with Convolutional Networks, Advances in Neural Information Processing Systems, 769–776.
• Hüsem, H., Orman, Z. (2020). A Survey on Image Super-Resolution with Generative Adversarial Networks, Acta Infologica, 4(2), 139-154.
• Donoho, D. L., Johnstone, J. M. (1994). Ideal spatial adaptation by wavelet shrinkage, Biometrika, 81(3), 425–455.
• Girdher, A. Goyal, B. Dogra, A. Dhindsa, A. Agrawal, S. (2019). Image Denoising: Issues and Challenges, Proceedings of International Conference on Advancements in Computing & Management (ICACM).
• Gu, S., Timofte, R. (2019). A Brief Review of Image Denoising Algorithms and Beyond, Inpainting and Denoising Challenges, The Springer Series on Challenges in Machine Learning, 1-21.