Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention

Huimin Qu; Haiyan Xie; Qianying Wang; Huimin Qu; Haiyan Xie; Qianying Wang

doi:10.3934/era.2025055

Electronic Research Archive

2025, Volume 33, Issue 3: 1231-1266. doi: 10.3934/era.2025055

Previous Article Next Article

Research article Special Issues

Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention

School of Science, Dalian Maritime University, Dalian 116026, China

Received: 27 December 2024 Revised: 01 February 2025 Accepted: 14 February 2025 Published: 03 March 2025

Medical image denoising is particularly important in brain image processing. Noise in acquisition and transmission degrades image quality and affects the reliability of diagnosis and research. Due to the complexity of the brain's structure and minor density differences, noise can increase diagnosis difficulty, so high-quality images are essential for disease detection, prognosis assessment, and treatment plan development. This paper proposes a multi-convolutional neural network based on feature distillation learning and dense residual attention to enhance the quality of brain images and improve denoising performance. The overall network structure contains four parts: a global sparse network (GSN), a dense residual attention network (DRAN), a feature distiller network (FDN), and a feature processing block (FPB). Before feeding the brain images into the denoising network model, they are preprocessed using a modified watershed algorithm based on a combination of a morphological gradient, Sobel's operator, and Canny's operator. The GSN is used to extract global features and increase the sensory field, and the DRAN efficiently extracts key features by combining improved channel attention and spatial attention mechanisms. The FDN extracts useful features through two feature distillation blocks, suppresses redundant information, and reduces computational complexity. The FPB performs feature fusion. Experimental results on brain image datasets and ground-based open datasets show that the proposed model outperforms existing methods in several metrics, and helps to improve the accuracy of brain disease diagnosis and treatment.

Keywords:

Citation: Huimin Qu, Haiyan Xie, Qianying Wang. Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention[J]. Electronic Research Archive, 2025, 33(3): 1231-1266. doi: 10.3934/era.2025055

Related Papers:

[1]	Sri Kurniati, Sudirman Syam, Arifin Sanusi . Numerical investigation and improvement of the aerodynamic performance of a modified elliptical-bladed Savonius-style wind turbine. AIMS Energy, 2023, 11(6): 1211-1230. doi: 10.3934/energy.2023055
[2]	Nour Khlaifat, Ali Altaee, John Zhou, Yuhan Huang . A review of the key sensitive parameters on the aerodynamic performance of a horizontal wind turbine using Computational Fluid Dynamics modelling. AIMS Energy, 2020, 8(3): 493-524. doi: 10.3934/energy.2020.3.493
[3]	Stephen K. Musau, Kathrin Stahl, Kevin Volkmer, Nicholas Kaufmann, Thomas H. Carolus . A design and performance prediction method for small horizontal axis wind turbines and its application. AIMS Energy, 2021, 9(5): 1043-1066. doi: 10.3934/energy.2021048
[4]	Majid Deldar, Afshin Izadian, Sohel Anwar . Analysis of a hydrostatic drive wind turbine for improved annual energy production. AIMS Energy, 2018, 6(6): 908-925. doi: 10.3934/energy.2018.6.908
[5]	Ammar E. Ali, Nicholas C. Libardi, Sohel Anwar, Afshin Izadian . Design of a compressed air energy storage system for hydrostatic wind turbines. AIMS Energy, 2018, 6(2): 229-244. doi: 10.3934/energy.2018.2.229
[6]	Hassam Nasarullah Chaudhry, John Kaiser Calautit, Ben Richard Hughes . The Influence of Structural Morphology on the Efficiency of Building Integrated Wind Turbines (BIWT). AIMS Energy, 2014, 2(3): 219-236. doi: 10.3934/energy.2014.3.219
[7]	Nadwan Majeed Ali, Handri Ammari . Design of a hybrid wind-solar street lighting system to power LED lights on highway poles. AIMS Energy, 2022, 10(2): 177-190. doi: 10.3934/energy.2022010
[8]	Azevedo Joaquim, Mendonça Fábio . Small scale wind energy harvesting with maximum power tracking. AIMS Energy, 2015, 2(3): 297-315. doi: 10.3934/energy.2015.3.297
[9]	Saiyad S. Kutty, M. G. M. Khan, M. Rafiuddin Ahmed . Estimation of different wind characteristics parameters and accurate wind resource assessment for Kadavu, Fiji. AIMS Energy, 2019, 7(6): 760-791. doi: 10.3934/energy.2019.6.760
[10]	Ayman B. Attya, T. Hartkopf . Wind Turbines Support Techniques during Frequency Drops — Energy Utilization Comparison. AIMS Energy, 2014, 2(3): 260-275. doi: 10.3934/energy.2014.3.260

Abstract

A correction on

Numerical investigation and improvement of the aerodynamic performance of a modified elliptical-bladed Savonius-style wind turbine. By Sri Kurniati, Sudirman Syam and Arifin Sanusi. AIMS Energy, 2023, Volume 11, Issue 6: 1211–1230. Doi: 10.3934/energy.2023055

The authors would like to make the following corrections to the published paper [1].

On page 1213, we updated the contents of "one symbol statement: ρ" in section 2. The updated contents are as follows:

- ρ is the the density of air,

On page 1215, we updated the contents of "Eq 16" in section 2. The updated contents are as follows:

$\frac{{{\bf{∂}} {\bf{ \pmb{\mathsf{ ϕ}} }}}_{{\bf{ℓ}}}}{{\bf{∂}} \boldsymbol{t}}+{\bf{∇}} \left({{\varnothing }}_{{\bf{ℓ}}}\overrightarrow{\boldsymbol{V}}\right)-{\bf{∇}} \left({\bf{\Gamma }}_{{\bf{ℓ}}}\overrightarrow{\boldsymbol{V}}{{\varnothing }}_{{\bf{ℓ}}}\right) = {\boldsymbol{R}}_{{\bf{ℓ}}}$

(16)

On page 1216, we updated the contents of "Table 2" in section 2. The updated contents are as follows:

Table 2. The terms in the general transfer equation Eq 16.

$\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \frac{{\partial {\rm{ \mathsf{ ϕ} }}}_{\ell }}{\partial t}+\nabla \left({\varnothing }_{\ell }\overrightarrow{V}\right)-\nabla \left({\mathrm{\Gamma }}_{\ell }\overrightarrow{V}{\varnothing }_{\ell }\right)={R}_{\ell } \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;(16)$
$l$	${\varnothing }_{\ell }$	${\mathrm{\Gamma }}_{\ell }$
1	U	$vt$	$-\frac{1}{\rho }\frac{\partial \rho }{\partial x}+\frac{\partial }{\partial x}\left(vt\frac{\partial u}{\partial x}\right)+\frac{\partial }{\partial y}\left(vt\frac{\partial v}{\partial x}\right)+\frac{\partial }{\partial z}\left(vt\frac{\partial w}{\partial x}\right)+gx$
2	V	$vt$	$-\frac{1}{\rho }\frac{\partial \rho }{\partial y}+\frac{\partial }{\partial x}\left(vt\frac{\partial u}{\partial y}\right)+\frac{\partial }{\partial y}\left(vt\frac{\partial v}{\partial y}\right)+\frac{\partial }{\partial z}\left(vt\frac{\partial w}{\partial y}\right)+gy$
3	W	$vt$	$-\frac{1}{\rho }\frac{\partial \rho }{\partial z}+\frac{\partial }{\partial x}\left(vt\frac{\partial u}{\partial z}\right)+\frac{\partial }{\partial y}\left(vt\frac{\partial v}{\partial z}\right)+\frac{\partial }{\partial z}\left(vt\frac{\partial w}{\partial z}\right)+gz$
4	1	0	0
5	K	$vt/$	$G-\varepsilon$
6	$\varepsilon$	$vt/$	${C}_{1}\frac{\varepsilon }{k}G-{c}_{2}\frac{\varepsilon }{k}\varepsilon$

| Show Table

DownLoad: CSV

Conflict of interest

All authors declare no conflicts of interest in this paper.

References

[1]	P. S. Chaitanya, S. K. Satpathy, A multilevel de-noising approach for precision edge-based fragmentation in MRI brain tumor segmentation, Trait. Signal, 40 (2023), 1715–1722. https://doi.org/10.18280/ts.400440 doi: 10.18280/ts.400440
[2]	K. Gong, K. Johnson, G. El Fakhri, Q. Li, T. Pan, PET image denoising based on denoising diffusion probabilistic model, Eur. J. Nucl. Med. Mol. Imaging, 51 (2024), 358–368. https://doi.org/10.1007/s00259-023-06417-8 doi: 10.1007/s00259-023-06417-8
[3]	H. Aetesam, S. K. Maji, Deep variational magnetic resonance image denoising via network conditioning, Biomed. Signal Process. Control, 95 (2024), 106452. https://doi.org/10.1016/j.bspc.2024.106452 doi: 10.1016/j.bspc.2024.106452
[4]	A. Sharma, V. Chaurasia, MRI denoising using advanced NLM filtering with non-subsampled shearlet transform, Signal Image Video Process., 15 (2021), 1331–1339. https://doi.org/10.1007/s11760-021-01864-y doi: 10.1007/s11760-021-01864-y
[5]	S. V. M. Sagheer, S. N. George, A review on medical image denoising algorithms, Biomed. Signal Process. Control, 61 (2020), 102036. https://doi.org/10.1016/j.bspc.2020.102036 doi: 10.1016/j.bspc.2020.102036
[6]	J. Zhang, W. Gong, L. Ye, F. Wang, Z. Shangguan, Y. Cheng, A review of deep learning methods for denoising of medical low-dose CT images, Comput. Biol. Med., 171 (2024), 108112. https://doi.org/10.1016/j.compbiomed.2024.108112 doi: 10.1016/j.compbiomed.2024.108112
[7]	J. Pan, Q. Zuo, B. Wang, C. L. P. Chen, B. Lei, S. Wang, DecGAN: Decoupling generative adversarial network for detecting abnormal neural circuits in Alzheimer's disease, IEEE Trans. Artif. Intell., 5 (2024), 5050–5063. https://doi.org/10.1109/TAI.2024.3416420 doi: 10.1109/TAI.2024.3416420
[8]	M. Li, R. Idoughi, B. Choudhury, W. Heidrich, Statistical model for OCT image denoising, Biomed. Opt. Express, 8 (2017), 3903–3917. https://doi.org/10.1364/BOE.8.003903 doi: 10.1364/BOE.8.003903
[9]	S. Dolui, A. Kuurstra, I. C. S. Patarroyo, O.V. Michailovich, A new similarity measure for non-local means filtering of MRI images, J. Visual Commun. Image Representation, 24 (2013), 1040–1054. https://doi.org/10.1016/j.jvcir.2013.06.011 doi: 10.1016/j.jvcir.2013.06.011
[10]	C. Swarup, K. U. Singh, A. Kumar, S. K. Pandey, N. Varshney, T. Singh, Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches, Electron. Res. Arch., 31 (2023), 2900–2924. https://doi.org/10.3934/era.2023146 doi: 10.3934/era.2023146
[11]	K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising, IEEE Trans. Image Process., 26 (2017), 3142–3155. https://doi.org/10.1109/TIP.2017.2662206 doi: 10.1109/TIP.2017.2662206
[12]	C. Tian, Y. Xu, L. Fei, J. Wang, J. Wen, N. Luo, Enhanced CNN for image denoising, CAAI Trans. Intell. Technol., 4 (2019), 17–23. https://doi.org/10.1049/trit.2018.1054 doi: 10.1049/trit.2018.1054
[13]	K. Zhang, W. Zuo, L. Zhang, FFDNet: Toward a fast and flexible solution for CNN-based image denoising, IEEE Trans. Image Process., 27 (2018), 4608–4622. https://doi.org/10.1109/TIP.2018.2839891 doi: 10.1109/TIP.2018.2839891
[14]	W. Jifara, F. Jiang, S. Rho, M. Cheng, S. Liu, Medical image denoising using convolutional neural network: A residual learning approach, J. Supercomput., 75 (2019), 704–718. https://doi.org/10.1007/s11227-017-2080-0 doi: 10.1007/s11227-017-2080-0
[15]	C. Tian, Y. Xu, W. Zuo, Image denoising using deep CNN with batch renormalization, Neural Networks, 121 (2020), 461–473. https://doi.org/10.1016/j.neunet.2019.08.022 doi: 10.1016/j.neunet.2019.08.022
[16]	M. S. Hema, Sowjanya, N. Sharma, G. Abhishek, G. Shivani, P. P. Kumar, Identification and classification of brain tumor using convolutional neural network with autoencoder feature selection, in International Conference on Emerging Technologies in Computer Engineering, 1591 (2022), 251–258. https://doi.org/10.1007/978-3-031-07012-9_22
[17]	C. Tian, M. Zheng, W. Zuo, B. Zhang, Y. Zhang, D. Zhang, Multi-stage image denoising with the wavelet transform, Pattern Recognit., 134 (2023), 109050. https://doi.org/10.1016/j.patcog.2022.109050 doi: 10.1016/j.patcog.2022.109050
[18]	T. Li, Z. Zhang, M. Zhu, Z. Cui, D. Wei, Combining transformer global and local feature extraction for object detection, Complex Intell. Syst., 10 (2024), 4897–4920. https://doi.org/10.1007/s40747-024-01409-z doi: 10.1007/s40747-024-01409-z
[19]	Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, Q. Hu, ECA-Net: Efficient channel attention for deep convolutional neural networks, in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020), 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155
[20]	D. Hong, C. Huang, C. Yang, J. Li, Y. Qian, C. Cai, FFA-DMRI: A network based on feature fusion and attention mechanism for brain MRI denoising, Front. Neurosci., 14 (2020), 577937. https://doi.org/10.3389/fnins.2020.577937 doi: 10.3389/fnins.2020.577937
[21]	Y. Chen, R. Xia, K. Yang, K. Zou, MFMAM: Image inpainting via multi-scale feature module with attention module, Comput. Vision Image Understanding, 238 (2024), 103883. https://doi.org/10.1016/j.cviu.2023.103883 doi: 10.1016/j.cviu.2023.103883
[22]	J. Deng, C. Hu, Recovering a clean background: A new progressive multi-scale CNN for image denoising, Signal Image Video Process., 18 (2024), 4541–4552. https://doi.org/10.1007/s11760-024-03093-5 doi: 10.1007/s11760-024-03093-5
[23]	M. Duong, B. N. Thi, S. Lee, M. Hong, Multi-branch network for color image denoising using dilated convolution and attention mechanisms, Sensors, 24 (2024), 3608. https://doi.org/10.3390/s24113608 doi: 10.3390/s24113608
[24]	Y. Zhang, Y. Tian, Y. Kong, B. Zhong, Y. Fu, Residual dense network for image restoration, IEEE Trans. Pattern Anal. Mach. Intell., 43 (2021), 2480–2495. https://doi.org/10.1109/TPAMI.2020.2968521 doi: 10.1109/TPAMI.2020.2968521
[25]	F. Gao, Y. Wang, Z. Yang, Y. Ma, Q. Zhang, Single image super-resolution based on multi-scale dense attention network, Soft Comput., 27 (2023), 2981–2992. https://doi.org/10.1007/s00500-022-07456-3 doi: 10.1007/s00500-022-07456-3
[26]	Y. Li, T. Xie, D. Mei, Application of convolutional neural networks for parallel multi-scale feature extraction in noise image denoising, IEEE Access, 12 (2024), 98599–98610. https://doi.org/10.1109/ACCESS.2024.3427143 doi: 10.1109/ACCESS.2024.3427143
[27]	Y. Zhang, C. Wang, X. Lv, Y. Song, Attention-driven residual-dense network for no-reference image quality assessment, Signal Image Video Process., 18 (2024), 537–551. https://doi.org/10.1007/s11760-024-03172-7 doi: 10.1007/s11760-024-03172-7
[28]	Z. Hui, X. Wang, X. Gao, Fast and accurate single image super-resolution via information distillation network, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 723–731. https://doi.org/10.1109/CVPR.2018.00082
[29]	J. Liu, J. Tang, G. Wu, Residual feature distillation network for lightweight image super-resolution, in European Conference on Computer Vision, 12537 (2020), 41–55. https://doi.org/10.1007/978-3-030-67070-2_2
[30]	Y. Zhang, Y. Liu, Q. Li, J. Wang, M. Qi, H. Sun, et al., A lightweight fusion distillation network for image deblurring and deraining, Sensors, 21 (2021), 5312. https://doi.org/10.3390/s21165312 doi: 10.3390/s21165312
[31]	Z. Zong, L. Zha, J. Jiang, X. Liu, Asymmetric information distillation network for lightweight super resolution, in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2022), 1248–1257. https://doi.org/10.1109/CVPRW56347.2022.00131
[32]	Z. Yu, K. Xie, C. Wen, J. He, W. Zhang, A lightweight image super-resolution reconstruction algorithm based on the residual feature distillation mechanism, Sensors, 24 (2024), 1049. https://doi.org/10.3390/s24041049 doi: 10.3390/s24041049
[33]	M. Jiang, C. You, M. Wang, H. Zhang, Z. Gao, D. Wu, et al., Controllable deep learning denoising model for ultrasound images using synthetic noisy image, in Computer Graphics International Conference, 14495 (2024), 297–308. https://doi.org/10.1007/978-3-031-50069-5_25
[34]	W. Wu, S. Liu, Y. Xia, Y. Zhang, Dual residual attention network for image denoising, Pattern Recognit., 149 (2024), 110291. https://doi.org/10.1016/j.patcog.2024.110291 doi: 10.1016/j.patcog.2024.110291
[35]	W. Wu, G. Lv, S. Liao, Y. Zhang, FEUNet: A flexible and effective U-shaped network for image denoising, Signal Image Video Process., 17 (2023), 2545–2553. https://doi.org/10.1007/s11760-022-02471-1 doi: 10.1007/s11760-022-02471-1
[36]	R. K. Thakur, S. K. Maji, Multi scale pixel attention and feature extraction based neural network for image denoising, Pattern Recognit., 141 (2023), 109603. https://doi.org/10.1016/j.patcog.2023.109603 doi: 10.1016/j.patcog.2023.109603
[37]	C. Tian, Y. Xu, W. Zuo, B. Du, C. Lin, D. Zhang, Designing and training of a dual CNN for image denoising, Knowl. Based Syst., 226 (2021), 106949. https://doi.org/10.1016/j.knosys.2021.106949 doi: 10.1016/j.knosys.2021.106949
[38]	J. Yang, H. Xie, N. Xue, A. Zhang, Research on underwater image denoising based on dual-channel residual network, Comput. Eng., 49 (2023), 188–198. https://doi.org/10.19678/j.issn.1000-3428.0064662 doi: 10.19678/j.issn.1000-3428.0064662
[39]	S. Ghaderi, S. Mohammadi, K. Ghaderi, F. Kiasat, M. Mohammadi, Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series, Annal. Med. Surg., 86 (2024), 1460–1475. https://doi.org/10.1097/MS9.0000000000001756 doi: 10.1097/MS9.0000000000001756
[40]	V. Sivakumar, N. Janakiraman, A novel method for segmenting brain tumor using modified watershed algorithm in MRI image with FPGA, Biosystems, 198 (2020), 104226. https://doi.org/10.1016/j.biosystems.2020.104226 doi: 10.1016/j.biosystems.2020.104226
[41]	Y. Liang, J. Fu, Watershed algorithm for medical image segmentation based on morphology and total variation model, Int. J. Pattern Recognit Artif Intell., 33 (2019), 1954019. https://doi.org/10.1142/S0218001419540193 doi: 10.1142/S0218001419540193
[42]	Y. Wu, Q. Li, The algorithm of watershed color image segmentation based on morphological gradient, Sensors, 22 (2022), 8202. https://doi.org/10.3390/s22218202 doi: 10.3390/s22218202
[43]	T. Yesmin, H. Lohiya, P. P. Acharjya, Detection and segmentation of brain tumor by using modified watershed algorithm and thresholding to reduce over-segmentation, in 2023 IEEE International Conference on Contemporary Computing and Communications (InC4), (2023), 1–6. https://doi.org/10.1109/InC457730.2023.10262891
[44]	X. Xu, S. Xu, L. Jin, E. Song, Characteristic analysis of Otsu threshold and its applications, Pattern Recognit. Lett., 32 (2011), 956–961. https://doi.org/10.1016/j.patrec.2011.01.021 doi: 10.1016/j.patrec.2011.01.021
[45]	C. Kumari, A. Mustafi, A novel radial kernel watershed basis segmentation algorithm for color image segmentation, Wireless Pers. Commun., 133 (2023), 2105–2124. https://doi.org/10.1007/s11277-023-10831-4 doi: 10.1007/s11277-023-10831-4
[46]	C. Zhang, J. Fang, Edge detection based on improved sobel operator, in Proceedings of the 2016 International Conference on Computer Engineering and Information Systems, (2016), 129–132. https://doi.org/10.2991/ceis-16.2016.25
[47]	B. Chen, W. Wu, Z. Li, T. Han, Z. Chen, W. Zhang, Attention-guided cross-modal multiple feature aggregation network for RGB-D salient object detection, Electron. Res. Arch., 32 (2024), 643–669. https://doi.org/10.3934/era.2024031 doi: 10.3934/era.2024031
[48]	Z. Cai, L. Xu, J. Zhang, Y. Feng, L. Zhu, F. Liu, ViT-DualAtt: An efficient pornographic image classification method based on Vision Transformer with dual attention, Electron. Res. Arch., 32 (2024), 6698–6716. https://doi.org/10.3934/era.2024313 doi: 10.3934/era.2024313
[49]	Y. Chen, R. Xia, K. Yang, K. Zou, DARGS: Image inpainting algorithm via deep attention residuals group and semantics, J. King Saud Univ. Comput. Inf. Sci., 35 (2023), 101567. https://doi.org/10.1016/j.jksuci.2023.101567 doi: 10.1016/j.jksuci.2023.101567
[50]	J. Yang, F. Xie, H. Fan, Z. Jiang, J. Liu, Classification for dermoscopy images using convolutional neural networks based on region average pooling, IEEE Access, 6 (2018), 65130–65138. https://doi.org/10.1109/ACCESS.2018.2877587 doi: 10.1109/ACCESS.2018.2877587
[51]	H. Wu, X. Gu, Max-pooling dropout for regularization of convolutional neural networks, in International Conference on Neural Information Processing, 9489 (2015), 46–54. https://doi.org/10.1007/978-3-319-26532-2_6
[52]	K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), 770–778. https://doi.org/10.1109/CVPR.2016.90
[53]	G. Huang, Z. Liu, L. V. D. Maaten, K. Q. Weinberger, Densely connected convolutional networks, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), 2261–2269. https://doi.org/10.1109/CVPR.2017.243
[54]	Z. Feng, Y. Chen, L. Xie, Unsupervised anomaly detection via knowledge distillation with non-directly-coupled student block fusion, Mach. Vision Appl., 34 (2023), 104. https://doi.org/10.1007/s00138-023-01454-7 doi: 10.1007/s00138-023-01454-7
[55]	X. Zhao, X. Cai, Y. Xue, Y. Liao, L. Lin, T. Zhao, UKD-Net: efficient image enhancement with knowledge distillation, J. Electron. Imaging, 33 (2024), 023024. https://doi.org/10.1117/1.JEI.33.2.023024 doi: 10.1117/1.JEI.33.2.023024
[56]	Y. Li, J. Cao, Z. Li, S. Oh, N. Komuro, Lightweight single image super-resolution with dense connection distillation network, ACM Trans. Multimedia Comput. Commun. Appl., 17 (2021), 1–17. https://doi.org/10.1145/3414838 doi: 10.1145/3414838
[57]	D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, preprint, arXiv: 1412.6980.
[58]	K. A. Johnson, J. A. Becker, The Whole Brain Atlas, 2005. Available from: https://www.med.harvard.edu/aanlib/home.html.
[59]	S. Roth, M. J. Black, Fields of experts: A framework for learning image priors, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2 (2005), 860–867. https://doi.org/10.1109/CVPR.2005.160
[60]	True Color Kodak Images, Kodak Lossless True Color Image Suite: PhotoCD PCD0992, 2013. Available from: https://r0k.us/graphics/kodak/.
[61]	A. Abdelhamed, S. Lin, M. S. Brown, A high-quality denoising dataset for smartphone cameras, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), 1692–1700. https://doi.org/10.1109/CVPR.2018.00182
[62]	M. Lebrun, M. Colom, J. Morel, The noise clinic: A blind image denoising algorithm, Image Process. On Line, 5 (2015), 1–54. https://doi.org/10.5201/ipol.2015.125 doi: 10.5201/ipol.2015.125

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)