Abstract
The utilization of CT scans in medical diagnostics has seen a consistent and substantial rise. However, this increased usage has raised concerns regarding the potential harmful effects of radiation exposure on patients. Reducing the radiation dose can result in more noise in the captured images, which can negatively impact the radiologist's ability to make accurate judgments with confidence. The most commonly encountered types of noise in medical images include Gaussian noise, speckle noise, and salt and pepper noise. Numerous significant efforts have been made to enhance image quality by eliminating this noise, and deep learning-based methods have gained popularity due to their effectiveness in handling various types of noise and image datasets. Within the research community, various neural network variations, such as autoencoders, generative adversarial networks (GANs), residual networks, convolutional neural networks (CNNs), and regularized neural networks, have gained immense popularity. In this paper, we comprehensively discuss eleven highly impactful approaches for image denoising based on deep learning techniques. We assess the performance of these methods using two quantitative and effective metrics: structural SIMilarity (SSIM) and peak signal-to-noise ratio (PSNR).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boyat AK, Joshi BK (2015) A review paper: noise models in digital image processing. Signal Image Process Int J 6 (2015)
Goyal B, Dogra A, Agrawal S, Sohi BS (2018) Noise issues prevailing in various types of medical images. Biomed Pharm J 11(3)
Boyat AK, Joshi BK (2014) Image denoising using wavelet transform and wiener filter based on log energy distribution over Poisson-Gaussian noise model. In: 2014 IEEE international conference on computational intelligence and computing research, pp 1–6
Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 2, pp 60–65
Paris S, Kornprobst P, Tumblin J, Durand F (2009) Bilateral filtering: theory and applications (bilateral filtering: theory and applications), p 1
Dabov K, Foi A, Katkovnik V, Egiazarian K (2006) Image denoising with block-matching and 3D filtering. Proc SPIE Int Soc Opt Eng 6064:354–365
Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imag 9(4):611–629
Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders, pp 1096–1103
Liu PY, Lam EY (2018) Image reconstruction using deep learning. arXiv preprint arXiv:1809.10410
Sze-To A, Tizhoosh HR, Wong AK (2016) Binary codes for tagging x-ray images via deep de-noising autoencoders, pp 2864–2871. https://doi.org/10.1109/IJCNN.2016.7727561
Yang Q et al (2018) Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss. IEEE Trans Med Imaging 37(6):1348–1357. https://doi.org/10.1109/TMI.2018.2827462
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition (Online). Available: arXiv: 1409.1556
Li Z, Huang J, Yu L, Chi Y, Jin M (2019) Low-dose CT image denoising using cycle-consistent adversarial networks. In: 2019 IEEE nuclear science symposium and medical imaging conference (NSS/MIC), 2019, pp 1–3. https://doi.org/10.1109/NSS/MIC42101.2019.9059965
Park HS, Baek J, You SK, Choi JK, Seo JK (2019) Unpaired image denoising using a generative adversarial network in X-ray CT. IEEE Access 7:110414–110425. https://doi.org/10.1109/ACCESS.2019.2934178
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), 2016, pp 770–778
Mao X-J, Shen C, Yang Y-B (2016) Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In: Presented at the Proceedings of the 30th international conference on neural information processing systems, Barcelona, Spain
Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26(7):3142–3155
Kang E, Chang W, Yoo J, Ye JC (2018) Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE Trans Med Imaging 37(6):1358–1369. https://doi.org/10.1109/TMI.2018.2823756
Ren C, He X, Pu Y, Nguyen TQ (2021) Learning image profile enhancement and denoising statistics priors for single-image super-resolution. IEEE Trans Cybern 51(7):3535–3548. https://doi.org/10.1109/TCYB.2019.2933257
Ge Y et al (2021) Enhancing the X-ray differential phase contrast image quality with deep learning technique. IEEE Trans Biomed Eng 68(6):1751–1758. https://doi.org/10.1109/TBME.2020.3011119
Sun H, Peng L, Zhang H, He Y, Cao S, Lu L (2021) Dynamic PET image denoising using deep image prior combined with regularization by denoising. IEEE Access 9:52378–52392. https://doi.org/10.1109/ACCESS.2021.3069236
Lempitsky V, Vedaldi A, Ulyanov D (2018) Deep image prior. In Proceedings of IEEE/CVF conference on computer vision and pattern recognition, pp 9446–9454
Tommasi T, Caputo B, Welter P, Güld MO, Deserno TM (2010) Overview of the clef 2009 medical image annotation track. In: Multilingual information access evaluation II. Multimedia experiments. Springer, pp 85–93
Xu J, Zhang L, Zuo W, Zhang D, Feng X (2015) Patch group based nonlocal self-similarity prior learning for image denoising. In Proc. IEEE international conference on computer vision, pp 244–252
Chen F, Zhang L, Yu H (2015) External patch prior guided internal clustering for image denoising. In: Proceedings of IEEE international conference on computer vision, pp 603–611
Liu H, Xiong R, Zhang J, Gao W (2015) Image denoising via adaptive soft-thresholding based on non-local samples. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 484–492
Gu S, Zhang L, Zuo W, Feng X (2014) Weighted nuclear norm minimization with application to image denoising. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 2862–2869
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2014) Imagenet large scale visual recognition challenge. arXiv:1409.0575
AAPM, Low dose CT grand challenge (2017) (Online). Available: http://www.aapm.org/GrandChallenge/LowDoseCT/#
Timofte R et al (2017) NTIRE 2017 challenge on single image super resolution: methods and results. In: Proceedings of IEEE conference on computer vision and pattern recognition workshops, Honolulu, HI, USA, pp 1110–1121
Varela F, Lachaux J-P, Rodriguez E, Martinerie J (2001) The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2(4):229–239
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Srivastava, A., Rana, H., Misra, M.K., Singh, Y.B. (2024). Residual Learning and Deep Learning Models for Image Denoising in Medical Applications. In: Chaturvedi, A., Hasan, S.U., Roy, B.K., Tsaban, B. (eds) Cryptology and Network Security with Machine Learning. ICCNSML 2023. Lecture Notes in Networks and Systems, vol 918. Springer, Singapore. https://doi.org/10.1007/978-981-97-0641-9_54
Download citation
DOI: https://doi.org/10.1007/978-981-97-0641-9_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0640-2
Online ISBN: 978-981-97-0641-9
eBook Packages: EngineeringEngineering (R0)