本研究旨在研究基於深度神經卷積網絡的尖端算法,用於修復受損的圖像,這些圖像可能受到多種失真的影響。在現實世界的計算機視覺應用中,我們經常面臨由於模糊、噪音、JPEG 壓縮和光線干擾等失真而導致的數字圖像品質不佳的問題。研究人員已經開發了許多圖像增強技術來克服這些問題。然而,大多數方法僅限於解決單一的失真問題。這篇論文的結構為三個獨特的討論主題。首先,涉及使用兩個不同的殘差卷積網絡模型在 CIE Lab 通道中修復曝光不足和曝光過度的圖像,其中一個模型通過處理亮度(L)通道來改善圖像照明,另一個模型則負責使用色度通道(a-b)還原圖像顏色。第二個主題是通過實施一個帶有 UNet 架構的多失真圖像淨化網絡(MDPNet)來恢復受到多種失真(模糊、噪音、JPEG 壓縮和曝光缺陷)的影響的圖像,並且不使用激活函數。最後,討論了在優化船舶類別分類方面進行無監督域適應(UDA),利用特徵增強技術,如去模糊和去噪,對目標域數據集(真實船舶圖像)進行操作,同時對於源域數據集(船舶建模圖像)應用了多種技術,包括數據集豐富化、圖像擴增和特徵混合。此外,在這種方法中,對於域分類,提出了 Margin Disparity Discrepancy 和Minimum Class Confusion 方法的組合。總的來說,實驗表明,本研究中開發的深度卷積模型優於那些尖端技術。

This study is to consider cutting-edge algorithms based on deep neural convolution networks to repair degraded images with more than one type of distortion. In real-world computer vision applications, we are often faced with the problem of poor digital image quality due to distortions such as blur, noise, JPEG compression, and lighting interference. Researchers have developed numerous image enhancement techniques to overcome these issues. However, most of their methods are limited to solving one distortion problem. This dissertation is structured into three distinct discussion themes. First, regarding repairing underexposed and overexposed images using two different residual convolution network models in the CIE Lab channel, where one model plays the role of improving image illumination by working on the Luminosity (L) channel, and the other model is responsible for restoring image color using the chromatic channel (a-b). The second topic is the restoration of images suffering from multiple distortions such as blur, noise, JPEG compression, and exposure defects by implementing a multi-distorted image purification network (MDPNet) with UNet architecture and without using activation functions. Finally, a discussion of unsupervised domain adaptation (UDA) in optimizing ship type classification by utilizing feature enhancement techniques such as deblurring and denoising to the target domain dataset (real ship images) while for the source domain dataset (ship modeling images) several techniques are applied including dataset enrichment, image augmentation, and feature mixing. In addition, for domain classification in this approach, a combination of the Margin Disparity Discrepancy and Minimum Class Confusion methods is proposed. Overall, the experiments show that the deep convolution models developed in this study outperform those cutting-edge techniques.

[1]. A. Szajna, M. Kostrzewski, K. Ciebiera, R. Stryjski, and W. Woźniak, “Application of
the Deep CNN-Based Method in Industrial System for Wire Marking Identification,”
Energies, vol. 14, no. 12, 2021.
[2]. M. Valizadeh and S. J. Wolff, “Convolutional Neural Network applications in additive
manufacturing: A review,” Adv. Ind. Manuf. Eng., vol. 4, p. 100072, 2022.
[3]. Z. Zhao and Y. Jiao, “A fault diagnosis method for rotating machinery based on CNN
with mixed information,” IEEE Trans. Ind. Informatics, pp. 1–11, 2022.
[4]. H. Yu, L. T. Yang, Q. Zhang, D. Armstrong, and M. J. Deen, “Convolutional neural
networks for medical image analysis: State-of-the-art, comparisons, improvement and
perspectives,” Neurocomputing, vol. 444, pp. 92–110, 2021.
[5]. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks:
an overview and application in radiology,” Insights Imaging, vol. 9, no. 4, pp. 611–629,
2018.
[6]. N. Tajbakhsh et al., “Convolutional Neural Networks for Medical Image Analysis: Full
Training or Fine Tuning?,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1299–1312,
2016.
[7]. C. Cui et al., “Tri-Branch Convolutional Neural Networks for Top-k Focused Academic
Performance Prediction,” IEEE Trans. Neural Networks Learn. Syst., pp. 1–12, 2022.
[8]. G. Li, F. Liu, Y. Wang, Y. Guo, L. Xiao, and L. Zhu, “A Convolutional Neural Network
(CNN) Based Approach for the Recognition and Evaluation of Classroom Teaching
Behavior,” Sci. Program., vol. 2021, p. 6336773, 2021.
[9]. P. Bhardwaj, P. K. Gupta, H. Panwar, M. K. Siddiqui, R. Morales-Menendez, and A.
Bhaik, “Application of Deep Learning on Student Engagement in e-learning
environments.,” Comput. Electr. Eng. an Int. J., vol. 93, p. 107277, Jul. 2021.
[10]. H. Ben Ameur, S. Boubaker, Z. Ftiti, W. Louhichi, and K. Tissaoui, “Forecasting
commodity prices: empirical evidence using deep learning tools,” Ann. Oper. Res., 2023.
[11]. C. Chen, P. Zhang, Y. Liu, and J. Liu, “Financial quantitative investment using
convolutional neural network and deep learning technology,” Neurocomputing, vol. 390,
pp. 384–390, 2020.
[12]. J. Yu, Y. Qiao, N. Shu, K. Sun, S. Zhou, and J. Yang, “Neural Network Based
Transaction Classification System for Chinese Transaction Behavior Analysis,” in 2019
80IEEE International Congress on Big Data (BigDataCongress), 2019, pp. 64–71.
[13]. D. Nallaperuma et al., “Online Incremental Machine Learning Platform for Big Data-
Driven Smart Traffic Management,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12,
pp. 4679–4690, 2019.
[14]. M. Arnaboldi and G. Azzone, “Data science in the design of public policies: dispelling
the obscurity in matching policy demand and data offer,” Heliyon, vol. 6, no. 6, p.
e04300, Jun. 2020.
[15]. H. Nazari, H. Alkhader, A. F. M. S. Akhter, and S. Hizal, “The Contribution of Deep
Learning for Future Smart Cities,” in Cybersecurity for Smart Cities: Practices and
Challenges, M. Ahmed and P. Haskell-Dowland, Eds. Cham: Springer International
Publishing, 2023, pp. 135–150.
[16]. R. Carrol1, “Computer Vision”, in Business Transformation, 2021, [online] available
at: https://www.ibm.com/blog/computer-vision/, (Accessed: 7 July 2023).
[17]. Great Learning Team, “Introduction to Image Processing | What is Image Processing?”,
in Great Learning Blog, 2022, [online] available at:
https://www.mygreatlearning.com/blog/introduction-to-image-processing-what-is-
image-processing/, (Accessed: 8 July 2023).
[18]. Emblog, “5 Common Issues With Image Processing”, in Eminenture.com Blog, 2019,
[online] available at: https://www.eminenture.com/blog/5-common-issues-with-image-
processing/, (Accessed: 8 July 2023).
[19]. R. Soundrapandiyan, S. C. Satapathy, C. M. PVSSR, and N. G. Nhu, “A comprehensive
survey on image enhancement techniques with special emphasis on infrared images”, in
Multimedia Tools and Applications, pp. 1-33, 2022.
[20]. L. Tao, C. Zhu, G. Xiang, Y. Li, H. Jia, and X. Xie, “LLCNN: A convolutional neural
network for low-light image enhancement,” in 2017 IEEE Visual Communications and
Image Processing (VCIP), 2017, pp. 1–4.
[21]. C. Dong, C. C. Loy, K. He, and X. Tang, “Image Super-Resolution Using Deep
Convolutional Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 2, pp.
295–307, 2016.
[22]. M. A. Souibgui and Y. Kessentini, “DE-GAN: A Conditional Generative Adversarial
Network for Document Enhancement,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44,
no. 3, pp. 1180–1191, 2022.
[23]. C. Ledig et al., “Photo-realistic single image super-resolution using a generative
adversarial network,” in Proceedings of the IEEE conference on computer vision and
81pattern recognition, 2017, pp. 4681–4690.
[24]. H. Li et al., “SRDiff: Single image super-resolution with diffusion probabilistic models,”
Neurocomputing, vol. 479, pp. 47–59, 2022.
[25]. D. Zhou, Z. Yang, and Y. Yang, “Pyramid Diffusion Models for Low-light Image
Enhancement,” arXiv Prepr. arXiv2305.10028, 2023.
[26]. Y. Zhang, Q. Xu, T. Wang, and L. Sun, “Limited Recurrent Neural Network for
Superresolution Image Reconstruction,” in Neural Information Processing, 2006, pp.
304–313.
[27]. D. Liu, B. Wen, Y. Fan, C. C. Loy, and T. S. Huang, “Non-local recurrent network for
image restoration,” Adv. Neural Inf. Process. Syst., vol. 31, 2018.
[28]. A. Rayan et al., “Utilizing CNN-LSTM techniques for the enhancement of medical
systems.,” Alexandria Engineering Journal, vol. 72. pp. 323–338, Jun-2023.
[29]. Z. Zhang, Y. Jiang, J. Jiang, X. Wang, P. Luo, and J. Gu, “STAR: A structure-aware
lightweight transformer for real-time image enhancement,” in Proceedings of the
IEEE/CVF International Conference on Computer Vision, 2021, pp. 4106–4115.
[30]. M. A. Souibgui et al., “DocEnTr: An end-to-end document image enhancement
transformer,” in 2022 26th International Conference on Pattern Recognition (ICPR),
2022, pp. 1699–1705.
[31]. J. Lim, “Common photography problems and how to fix them”, in Creatively Squared
website, 2023, [online] Available at:
https://www.creativelysquared.com/article/common-photography-problems-and-how-
to-fix-them, (Accessed: 14 July 2023).
[32]. A. Kun, “Top 10 Digital Photography Mistakes”, in Exposure Guide website, 2012,
[online] Available at: https://www.exposureguide.com/top-10-digital-photography-
mistakes/, (Accessed: 14 July 2023).
[33]. G. Stoker, “25 common Photography problems and how to fix them”, in Digital Camera
World, 2018, [online] Available at: https://www.digitalcameraworld.com/features/25-
common-photography-problems-and-how-to-fix-them, (Accessed: 14 July 2023).
[34]. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D.
Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural
computation, vol. 1, no. 4, pp. 541–551, 1989.
[35]. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep
convolutional neural networks,” Advances in neural information processing systems, vol.
25, 2012.
82[36]. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke,
and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE
conference on computer vision and pattern recognition, pp. 1–9, 2015.
[37]. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale
image recognition,” arXiv preprint arXiv: 1409.1556, 2014.
[38]. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–
778, 2016.
[39]. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical
image segmentation,” in Medical Image Computing and Computer-Assisted
Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October
5-9, 2015, Proceedings, Part III 18, pp. 234–241, Springer, 2015.
[40]. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-
time object detection,” in Proceedings of the IEEE conference on computer vision and
pattern recognition, pp. 779–788, 2016.
[41]. AThomas Payne, “Another Photography Book,” Version 2020-7: Creative Commons
licence, 2018, pp. 121–122.
[42]. S. Wang, J. Zheng, H. -M. Hu and B. Li, "Naturalness Preserved Enhancement Algorithm
for Non-Uniform Illumination Images," in IEEE Transactions on Image Processing, vol.
22, no. 9, pp. 3538-3548, Sept. 2013.
[43]. X. Guo, Y. Li and H. Ling, "LIME: Low-Light Image Enhancement via Illumination
Map Estimation," in IEEE Transactions on Image Processing, vol. 26, no. 2, pp. 982-993,
Feb. 2017.
[44]. Q. Song and P. C. Cosman, "Luminance Enhancement and Detail Preservation of Images
and Videos Adapted to Ambient Illumination," in IEEE Transactions on Image
Processing, vol. 27, no. 10, pp. 4901-4915, Oct. 2018.
[45]. E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, ‘’Photographic tone reproduction for
digital images,” ACM Transaction on Graphics. vol. 21, no. 3, pp. 267–276, July 2002.
[46]. X. Fu, Y. Liao, D. Zeng, Y. Huang, X. -P. Zhang and X. Ding, "A Probabilistic Method
for Image Enhancement With Simultaneous Illumination and Reflectance Estimation,"
in IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 4965-4977, Dec. 2015.
[47]. M. Abdullah-Al-Wadud, M. H. Kabir, M. A. Akber Dewan and O. Chae, "A Dynamic
Histogram Equalization for Image Contrast Enhancement," in IEEE Transactions on
Consumer Electronics, vol. 53, no. 2, pp. 593-600, May 2007.
83[48]. M. Veluchamy and B. Subramani, "Image contrast and color enhancement using adaptive
gamma correction and histogram equalization," Optik, vol. 183, pp. 329-337, 2019.
[49]. C. Li, S. Tang, J. Yan, and T. Zhou, "Low-Light Image Enhancement Based on Quasi-
Symmetric Correction Functions by Fusion," Symmetry vol. 12, no. 9, pp. 1561, 2020.
[50]. W. Zhang, X. Liu, W. Wang, and Y. Zeng, “Multi-exposure image fusion based on
wavelet transform,” International Journal of Advanced Robotic Systems, March 2018,
DOI: 10.1177/1729881418768939.
[51]. C. Jung, Q. Yang, T. Sun, Q. Fu, and H. Song, "Low light image enhancement with dual-
tree complex wavelet transform," Journal of Visual Communication and Image
Representation, vol. 42, pp. 28-36, 2017.
[52]. H. Demirel, C. Ozcinar and G. Anbarjafari, "Satellite Image Contrast Enhancement
Using Discrete Wavelet Transform and Singular Value Decomposition," in IEEE
Geoscience and Remote Sensing Letters, vol. 7, no. 2, pp. 333-337, April 2010.
[53]. D. J. Jobson, Z. Rahman and G. A. Woodell, "Properties and performance of a
center/surround retinex," in IEEE Transactions on Image Processing, vol. 6, no. 3, pp.
451-462, March 1997.
[54]. D. J. Jobson, Z. Rahman and G. A. Woodell, "A multiscale retinex for bridging the gap
between color images and the human observation of scenes," in IEEE Transactions on
Image Processing, vol. 6, no. 7, pp. 965-976, July 1997, doi: 10.1109/83.597272.
[55]. Jia Li, "Application of image enhancement method for digital images based on Retinex
theory," Optik, vol. 124, no. 23, pp. 5986-5988, 2013.
[56]. M. Li, J. Liu, W. Yang, X. Sun and Z. Guo, "Structure-Revealing Low-Light Image
Enhancement Via Robust Retinex Model," in IEEE Transactions on Image Processing,
vol. 27, no. 6, pp. 2828-2841, June 2018.
[57]. X. Ren, W. Yang, W. -H. Cheng and J. Liu, "LR3M: Robust Low-Light Enhancement
via Low-Rank Regularized Retinex Model," in IEEE Transactions on Image Processing,
vol. 29, pp. 5862-5876, 2020.
[58]. K. G. Lore, A. Akintayo, S. Sarkar, "LLNet: A deep autoencoder approach to natural
low-light image enhancement," Pattern Recognition, vol. 61, pp. 650-662, 2017.
[59]. L. Jiang, Y. Jing, S. Hu, B. Ge, and W. Xiao, “Deep refinement network for natural low-
light image enhancement in symmetric pathways,” Symmetry, vol. 10, no. 10, p. 491,
2018.
84[60]. Q. Li, H. Wu, L. Xu, L. Wang, Y. Lv, and X. Kang, “Low-light image enhancement
based on deep symmetric encoder–decoder convolutional networks,” Symmetry, vol. 12,
no. 3, p. 446, 2020.
[61]. Y. Guo, X. Ke, J. Ma and J. Zhang, "A Pipeline Neural Network for Low-Light Image
Enhancement," in IEEE Access, vol. 7, pp. 13737-13744, 2019.
[62]. C. Li, J. Guo, F. Porikli, Y. Pang, "LightenNet: A Convolutional Neural Network for
weakly illuminated image enhancement," Pattern Recognition Letters, vol. 104, pp. 15-
22, 2018.
[63]. R. Wang, Q. Zhang, C. -W. Fu, X. Shen, W. S. Zheng and J. Jia, "Underexposed Photo
Enhancement Using Deep Illumination Estimation," 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6842-6850.
[64]. C. Li, C. Guo and C. L. Chen, "Learning to Enhance Low-Light Image via Zero-
Reference Deep Curve Estimation," in IEEE Transactions on Pattern Analysis and
Machine Intelligence, 2021, DOI: 10.1109/TPAMI.2021.3063604. [online]
[65]. Z. Gao, E. Edirisinghe, and S. Chesnokov, “OEC-cnn: a simple method for over-exposure
correction in photographs,” in IS&T International Symposium on Electronic Imaging
2020, DOI: 10.2352/ISSN.2470-1173.2020.10.IPAS-182.
[66]. J. Wang, W. Tan, X. Niu and B. Yan, "RDGAN: Retinex Decomposition Based
Adversarial Learning for Low-Light Enhancement," in IEEE International Conference
on Multimedia and Expo (ICME), 2019, pp. 1186-1191.
[67]. Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang,
"EnlightenGAN: Deep Light Enhancement Without Paired Supervision," in IEEE
Transactions on Image Processing, vol. 30, pp. 2340-2349, 2021.
[68]. T. Ma, M. Guo, Z. Yu, Y. Chen, X. Ren, R. Xi, Y. Li, and X. Zhou, “Retinexgan:
Unsupervised low-light enhancement with two-layer convolutional decomposition
networks,” IEEE Access, vol. 9, pp. 56539–56550, 2021
[69]. Y. Cao, R. Yurui, T. H. Li, and G. Li., "Over-exposure Correction via Exposure and
Scene Information Disentanglement," In Proceedings of the Asian Conference on
Computer Vision, 2020.
[70]. Q. Zhang, Y. Nie, and W. Zheng, “Dual Illumination Estimation for Robust Exposure
Correction,” In Computer Graphics Forum, vol. 38, no. 7, pp. 243-252, October 2019.
[71]. X. Fu, D. Zeng, Y. Huang, Y. Liao, X. Ding, and J. Paisley, “A fusion-based enhancing
method for weakly illuminated images,” Signal Processing, vol.129, pp. 82-96, 2016.
[72]. C. R. Steffens, L. R. V. Messias, P. Drews-Jr and S. S. d. C. Botelho, "Contrast
85Enhancement and Image Completion: A CNN Based Model to Restore Ill Exposed
Images," in IEEE 17th International Conference on Industrial Informatics (INDIN), pp.
226-232, 2019.
[73]. S. Goswami, and S. K. Singh,” A simple deep learning based image illumination
correction method for paintings,” Pattern Recognition Letters, vol. 138, pp.392-396,
2020.
[74]. X. Li, B. Zhang, J. Liao, and P. V. Sander, “Document Rectification and Illumination
Correction using a Patch-based CNN,” ACM Trans. Graph, vol. 38, no. 6, Article 168,
2019, [online] available: https://doi.org/10.1145/3355089.3356563.
[75]. L. Ma, D. Jin, R. Liu, X. Fan and Z. Luo, "Joint Over and Under Exposures Correction
by Aggregated Retinex Propagation for Image Enhancement," in IEEE Signal Processing
Letters, vol. 27, pp. 1210-1214, 2020.
[76]. M. Afifi, K. G. Derpanis, B. Ommer, and M. S. Brown,” Learning Multi-Scale Photo
Exposure Correction. In Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition, 2021, pp. 9157-9167.
[77]. Y. Shen, V. S. Sheng, L. Wang, J. Duan, X. Xi, D. Zhang, and Z. Cui, “Empirical
comparisons of deep learning networks on liver segmentation,” Comput. Mater. Contin,
vol. 62, pp. 1233–1247, 2020.
[78]. Y. Cao, S. Liu, Y. Peng, and J. Li, “Denseunet: densely connected unet for electron
microscopy image segmentation,” IET Image Processing, vol. 14, no. 12, pp. 2682–2689,
2020.
[79]. Y. Tai, J. Yang, X. Liu, and C. Xu, “Memnet: A persistent memory network for image
restoration,” in Proceedings of the IEEE international conference on computer vision, pp.
4539–4547, 2017.
[80]. Y. Atoum, M. Ye, L. Ren, Y. Tai, and X. Liu, “Color-wise attention network for low-
light image enhancement,” in Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition Workshops, pp. 506–507, 2020.
[81]. Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image
restoration,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no.
7, pp. 2480–2495, 2020.
[82]. V. Bychkovsky, S. Paris, E. Chan, and F. Durand, “Learning photographic global tonal
adjustment with a database of input / output image pairs,” in The Twenty-Fourth IEEE
Conference on Computer Vision and Pattern Recognition, 2011.
[83]. M. Everingham and J. Winn, “The pascal visual object classes challenge 2012 (voc2012)
86development kit,” Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep, vol. 2007, no.
1-45, p. 5, 2012.
[84]. H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative
adversarial networks,” in International conference on machine learning, pp. 7354–7363,
PMLR, 2019.
[85]. Z. Huang, Z. Chen, Q. Zhang, G. Quan, M. Ji, C. Zhang, Y. Yang, X. Liu, D. Liang, H.
Zheng, et al., “Cagan: A cycle-consistent generative adversarial network with attention
for low-dose ct imaging,” IEEE Transactions on Computational Imaging, vol. 6, pp.
1203–1218, 2020.
[86]. M. Guo, H. Lan, C. Yang, J. Liu, and F. Gao, “As-net: fast photoacoustic reconstruction
with multi-feature fusion from sparse data,” IEEE Transactions on Computational
Imaging, vol. 8, pp. 215–223, 2022.
[87]. Z. Jin, M. Z. Iqbal, D. Bobkov, W. Zou, X. Li, and E. Steinbach, “A flexible deep cnn
framework for image restoration,” IEEE Transactions on Multimedia, vol. 22, no. 4, pp.
1055–1068, 2019.
[88]. J. Wang, X. Wang, P. Zhang, S. Xie, S. Fu, Y. Li, and H. Han, “Correction of uneven
illumination in color microscopic image based on fully convolutional network,” Optics
express, vol. 29, no. 18, pp. 28503–28520, 2021.
[89]. B. Kim, H. Jung, and K. Sohn, “Multi-exposure image fusion using cross-attention
mechanism,” in 2022 IEEE International Conference on Consumer Electronics (ICCE),
pp. 1–6, IEEE, 2022.
[90]. S. Yoo, H. Bahng, S. Chung, J. Lee, J. Chang, and J. Choo, “Coloring with limited data:
Few-shot colorization via memory augmented networks,” in Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11283–11292,
2019.
[91]. R. Zhang, J.-Y. Zhu, P. Isola, X. Geng, A. S. Lin, T. Yu, and A. A. Efros, “Real-time
user-guided image colorization with learned deep priors,” arXiv preprint arXiv:1705.02999, 2017.
[92]. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment:
from error visibility to structural similarity,” IEEE transactions on image processing, vol.
13, no. 4, pp. 600–612, 2004.
[93]. C. Shi and Y. Lin, “Full reference image quality assessment based on visual salience with
color appearance and gradient similarity,” IEEE Access, vol. 8, pp. 97310–97320, 2020.
[94]. R. Hammell, “Ships in satellite 87 imagery,” 2018. Available
online:https://doi.org/10.34740/KAGGLE/DSV/61115 (accessed on 20 May 2023).
[95]. C.-W. Kuo, J. D. Ashmore, D. Huggins, and Z. Kira, “Data-efficient graph embedding
learning for pcb component detection,” in 2019 IEEE Winter Conference on Applications
of Computer Vision (WACV), pp. 551–560, IEEE, 2019.
[96]. S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. K. Singh, Z. Xue, A. Karargyris,
S. Antani, G. Thoma, and C. J. McDonald, “Lung segmentation in chest radiographs
using anatomical atlases with nonrigid registration,” IEEE transactions on medical
imaging, vol. 33, no. 2, pp. 577–590, 2013.
[97]. K. Zhang, X. Gao, D. Tao, and X. Li, “Single image super-resolution with non-local
means and steering kernel regression,” IEEE Transactions on Image Processing, vol. 21,
no. 11, pp. 4544–4556, 2012.
[98]. C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep
convolutional networks,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 38, no. 2, pp. 295–307, 2016.
[99]. Y. Shen, Q. Liu, S. Lou, and Y.-L. Hou, “Wavelet-based total variation and nonlocal
similarity model for image denoising,” IEEE Signal Processing Letters, vol. 24, no. 6,
pp. 877–881, 2017.
[100]. C. Tian, Y. Xu, and W. Zuo, “Image denoising using deep cnn with batch
renormalization,” Neural Networks, vol. 121, pp. 461–473, 2020.
[101]. W. Ren, X. Cao, J. Pan, X. Guo, W. Zuo, and M.-H. Yang, “Image deblurring via
enhanced low-rank prior,” IEEE Transactions on Image Processing, vol. 25, no. 7, pp.
3426–3437, 2016.
[102]. J. Hai, R. Yang, Y. Yu, and S. Han, “Combining spatial and frequency information for
image deblurring,” IEEE Signal Processing Letters, vol. 29, pp. 1679–1683, 2022.
[103]. H. Chang, M. K. Ng, and T. Zeng, “Reducing artifacts in jpeg decompression via a
learned dictionary,” IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 718–
728, 2014.
[104]. S. Zini, S. Bianco, and R. Schettini, “Deep residual autoencoder for blind universal jpeg
restoration,” IEEE Access, vol. 8, pp. 63283–63294, 2020. [9] Q. Zhang, Y. Nie, and W.-
S. Zheng, “Dual illumination estimation for robust exposure correction,” Computer
Graphics Forum, vol. 38, no. 7, pp. 243–252.
[105]. L. Zhang, K. Bronik, and B. W. Papiez, “Learning to restore multiple ̇ image
degradations simultaneously,” Pattern Recognition, vol. 136, p. 109250, 2023.
[106]. M. Suganuma, X. Liu, and T. Okatani, “Attention-based adaptive selection of
88operations for image restoration in the presence of unknown combined distortions,” in
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
pp. 9039–9048, 2019.
[107]. Z. Huang, C. Li, F. Duan, and Q. Zhao, “Multi-distorted image restoration with tensor
1× 1 convolutional layer,” in 2021 International Joint Conference on Neural Networks
(IJCNN), pp. 1–8, IEEE, 2021.
[108]. X. Li, X. Jin, J. Lin, S. Liu, Y. Wu, T. Yu, W. Zhou, and Z. Chen, “Learning
disentangled feature representation for hybrid-distorted image restoration,” in Computer
Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020,
Proceedings, Part XXIX 16, pp. 313–329, Springer, 2020.
[109]. Y. Wang, H. Li, and S. Hou, “Distortion detection and removal integrated method for
image restoration,” Digital Signal Processing, vol. 127, p. 103528, 2022.
[110]. K. Yu, X. Wang, C. Dong, X. Tang, and C. C. Loy, “Path-restore: Learning network
path selection for image restoration,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 44, no. 10, pp. 7078– 7092, 2021.
[111]. K. Yu, C. Dong, L. Lin, and C. C. Loy, “Crafting a toolchain for image restoration by
deep reinforcement learning,” in Proceedings of the IEEE conference on computer vision
and pattern recognition, pp. 2443–2452, 2018.
[112]. S. Kim, N. Ahn, and K.-A. Sohn, “Restoring spatially-heterogeneous distortions using
mixture of experts network,” in Computer Vision – ACCV 2020 (H. Ishikawa, C.-L. Liu,
T. Pajdla, and J. Shi, eds.), (Cham), pp. 185–201, Springer International Publishing,
2021.
[113]. W. Shin, N. Ahn, J.-H. Moon, and K.-A. Sohn, “Exploiting distortion information for
multi-degraded image restoration,” in Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition, pp. 537–546, 2022.
[114]. J. Zhang, J. Huang, M. Yao, Z. Yang, H. Yu, M. Zhou, and F. Zhao, “Ingredient-
oriented multi-degradation learning for image restoration,” in Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5825–5835,
2023.
[115]. T. Shen, C. Gou, J. Wang, and F.-Y. Wang, “Simultaneous segmentation and
classification of mass region from mammograms using a mixed-supervision guided deep
model,” IEEE Signal Processing Letters, vol. 27, pp. 196–200, 2020.
[116]. W. Baccouch, S. Oueslati, B. Solaiman, and S. Labidi, “A comparative study of cnn
and u-net performance for automatic segmentation of medical images: application to
89cardiac mri,” Procedia Computer Science, vol. 219, pp. 1089–1096, 2023. CENTERIS –
International Conference on ENTERprise Information Systems / ProjMAN –
International Conference on Project MANagement / HCist – International Conference on
Health and Social Care Information Systems and Technologies 2022.
[117]. G. Dulac-Arnold, D. Mankowitz, and T. Hester, “Challenges of realworld
reinforcement learning,” arXiv preprint arXiv:1904.12901, 2019.
[118]. L. Cai, H. Gao, and S. Ji, “Multi-stage variational auto-encoders for coarse-to-fine
image generation,” in Proceedings of the 2019 SIAM International Conference on Data
Mining, pp. 630–638, SIAM, 2019.
[119]. F. D. Keles, P. M. Wijewardena, and C. Hegde, “On the computational complexity of
self-attention,” in International Conference on Algorithmic Learning Theory, pp. 597–
619, PMLR, 2023.
[120]. L. Chen, X. Chu, X. Zhang, and J. Sun, “Simple baselines for image restoration,” in
European Conference on Computer Vision, pp. 17–33, Springer, 2022.
[121]. Q. Hou, D. Zhou, and J. Feng, “Coordinate attention for efficient mobile network
design,” in Proceedings of the IEEE/CVF conference on computer vision and pattern
recognition, pp. 13713–13722, 2021.
[122]. D. Misra, T. Nalamada, A. U. Arasanipalai, and Q. Hou, “Rotate to attend:
Convolutional triplet attention module,” in Proceedings of the IEEE/CVF winter
conference on applications of computer vision, pp. 3139–3148, 2021.
[123]. M. Mackay, B. D. Hardesty, and C. Wilcox, “The intersection between illegal fishing,
crimes at sea, and social well-being,” in Frontiers in Marine Science, vol. 7, p. 589000,
2020.
[124]. X. Hou, W. Ao, Q. Song, J. Lai, H. Wang, and F. Xu, "FUSAR-Ship: Building a high-
resolution SAR-AIS matchup dataset of Gaofen-3 for ship detection and recognition.,"
in Science China Information Sciences, vol. 63, pp. 1-19, 2020.
[125]. S. Sun, Y. Gu, and M. Ren, “Fine-Grained Ship Recognition from the Horizontal View
Based on Domain Adaptation,” in Sensors, vol. 22, no. 9, p. 3243, 2022.
[126]. S. Bagui, S. Brown, C. Hall, and R. Galliera, “Object Detection and Ship Classification
Using YOLOv5,” in BOHR International Journal of Computer Science, vol. 2, no. 1, pp.
124-133, 2022.
[127]. S. Kızılkaya, U. Alganci, and E. Sertel, “VHRShips: An Extensive Benchmark Dataset
for Scalable Deep Learning-Based Ship Detection Applications,” in ISPRS International
Journal of Geo-Information, vol. 11, no. 8, p. 445, 2022.
90[128]. D. Zhang, J. Zhan, L. Tan, Y. Gao, and R. Župan, “Comparison of two deep learning
methods for ship target recognition with optical remotely sensed data,” in Neural
Computing and Applications, vol. 33, pp. 4639- 4649, 2021.
[129]. Z. Xu, J. Sun, and Y. Huo, “Ship images detection and classification based on
convolutional neural network with multiple feature regions,” In IET Signal Processing,
vol. 16, no. 6, pp. 707-721, 2022.
[130]. C. M. Ward, J. Harguess, and C. Hilton, “Ship classification from overhead imagery
using synthetic data and domain adaptation,” In OCEANS 2018 MTS/IEEE Charleston,
pp. 1-5, October, 2018.
[131]. V. Gupta, M. Gupta, and P. Singla, “Ship detection from highly cluttered images using
convolutional neural network,” in Wireless Personal Communications, vol. 121, pp. 287-
305, 2021.
[132]. H. Bi, Z. Liu, J. Deng, Z. Ji, and J. Zhang, “Contrastive Domain Adaptation-Based
Sparse SAR Target Classification under Few-Shot Cases,” in Remote Sensing, vol. 15,
no. 2, p. 469, 2023.
[133]. M. Rostami, S. Kolouri, E. Eaton, and K. Kim, “Sar image classification using few-
shot cross-domain transfer learning,” In Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition Workshops, pp. 0-0, 2019.
[134]. F. Ucar, and D. Korkmaz, “A novel ship classification network with cascade deep
features for line-of-sight sea data,” in Machine Vision and Applications, vol. 32, no. 3,
p. 73, 2021.
[135]. F. Sharifzadeh, G. Akbarizadeh, and Y. Seifi Kavian, “Ship classification in SAR
images using a new hybrid CNN–MLP classifier,” in Journal of the Indian Society of
Remote Sensing, vol. 47, pp. 551-562, 2019.
[136]. K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, “Maximum classifier discrepancy
for unsupervised domain adaptation,” In Proceedings of the IEEE conference on
computer vision and pattern recognition, pp. 3723- 3732, 2018.
[137]. Y. Zhang, T. Liu, M. Long, and M. Jordan, “Bridging theory and algorithm for domain
adaptation,” In International conference on machine learning, pp. 7404-7413, May, 2019.
[138]. W. Wang, H. Li, Z. Ding, and Z. Wang, “Rethink maximum mean discrepancy for
domain adaptation,” in arXiv preprint arXiv, article: 2007.00689, 2020.
[139]. M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep
adaptation networks,” In International conference on machine learning, pp. 97-105, June,
2015.
91[140]. Y. Ganin, and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,”
In International conference on machine learning, pp. 1180-1189, June, 2015.
[141]. E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain
adaptation,” In Proceedings of the IEEE conference on computer vision and pattern
recognition, pp. 7167-7176, 2017.
[142]. M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint
adaptation networks,” In International conference on machine learning, pp. 2208-2217,
July, 2017.
[143]. M. Long, Z. Cao, J. Wang, and M. I. Jordan, “Conditional adversarial domain
adaptation,” Advances in neural information processing systems, vol. 31, 2018.
[144]. B. Sun, and K. Saenko, “Deep coral: Correlation alignment for deep domain
adaptation,” In Computer Vision–ECCV 2016 Workshops: Amsterdam, The
Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part III 14, pp. 443-450.
[145]. Y. Wang, W. Li, D. Dai, and L. Van Gool, “Deep domain adaptation by geodesic
distance minimization,” In Proceedings of the IEEE International Conference on
Computer Vision Workshops, pp. 2651- 2657, 2017.
[146]. Y. Zhang, N. Wang, S. Cai, and L. Song, “Unsupervised domain adaptation by mapped
correlation alignment,” IEEE Access, vol. 6, pp. 44698-44706, 2018.
[147]. A. Jain, “Game of Deep Learning: ShipAV datasets (Kaggle),” online: https://
www.kaggle.com/arpitjain007/game-of-deep-learning-shipdatasets
[148]. H. Zhang, H., M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical
risk minimization,” in arXiv preprint arXiv, p. 1710.09412, 2017.
[149]. Y. Jin, X. Wang, M. Long, and J. Wang, “Minimum class confusion for versatile
domain adaptation,” In Computer Vision–ECCV 2020: 16th European Conference,
Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI, vol. 16, pp. 464-480, 2020.
[150]. Image.cv, “speedboat dataset,” accessed: December 19, 2020, [Online]
https://images.cv/dataset/speedboat-image-classification-dataset.
[151]. S. Nah, S. Baik, S. Hong, G. Moon, S. Son, R. Timofte, and K. Mu Lee, “Ntire 2019
challenge on video deblurring and super-resolution: Dataset and study,” In Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops,
pp. 0-0, 2019.
[152]. A. Abdelhamed, S. Lin, and M. S. Brown, “A high-quality denoising dataset for
smartphone cameras,” In Proceedings of the IEEE conference on computer vision and
pattern recognition, pp. 1692-1700, 2018.
92[153]. A. Alekseev, “Blur Dataset,” accessed: October 20, 2022, [online] available:
https://www.kaggle.com/datasets/kwentar/blur-dataset
[154]. E. Mavridaki, V. Mezaris, "No-Reference blur assessment in natural images using
Fourier transform and spatial pyramids," in Proceeding of IEEE International Conference
on Image Processing (ICIP 2014), Paris, France, October 2014.
[155]. J. Anaya, and A. Barbu, “Renoir–a dataset for real low-light image noise reduction,” in
Journal of Visual Communication and Image Representation, vol. 51, pp. 144-154, 2018.
[156]. V. N. Vapnik, and A. Y. Chervonenkis, “On the uniform convergence of relative
frequencies of events to their probabilities,” in Measures of complexity: festschrift for
alexey chervonenkis, pp. 11-30, 2015.
[157]. X. Chen, S. Wang, M. Long, and J. Wang, “Transferability vs. discriminability: Batch
spectral penalization for adversarial domain adaptation, “In International conference on
machine learning, pp. 1081- 1090), May, 2019.
[158]. R. Xu, G. Li, J. Yang, and L. Lin, “Larger norm more transferable: An adaptive feature
norm approach for unsupervised domain adaptation,” In Proceedings of the IEEE/CVF
International Conference on Computer Vision, pp. 1426-1435, 2019.
[159]. K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C. A. Raffel, and C. L. Li,
“Fixmatch: Simplifying semi-supervised learning with consistency and confidence,” in
Advances in neural information processing systems, vol. 33, pp. 96-608, 2020.
[160]. Z. Yi, W. Shang, T. Xu and X. Wu, "Neighborhood Preserving and Weighted Subspace
Learning Method for Drift Compensation in Gas Sensor," in IEEE Transactions on
Systems, Man, and Cybernetics: Systems, vol. 52, no. 6, pp. 3530-3541, June 2022.
[161]. H. Yan, Z. Li, Q. Wang, P. Li, Y. Xu and W. Zuo, "Weighted and ClassSpecific
Maximum Mean Discrepancy for Unsupervised Domain Adaptation," in IEEE
Transactions on Multimedia, vol. 22, no. 9, pp. 2420-2433, 2020.
[162]. X. Ma, T. Zhang and C. Xu, "Deep Multi-Modality Adversarial Networks for
Unsupervised Domain Adaptation," in IEEE Transactions on Multimedia, vol. 21, no. 9,
pp. 2419-2431, 2019.
[163]. Z. -H. Liu, B. -L. Lu, H. -L. Wei, L. Chen, X. -H. Li and M. Rätsch, "Deep Adversarial
Domain Adaptation Model for Bearing Fault Diagnosis," in IEEE Transactions on
Systems, Man, and Cybernetics: Systems, vol. 51, no. 7, pp. 4217-4226, July 2021.
[164]. S. Yao, Q. Kang, M. Zhou, M. J. Rawa and A. Albeshri, "Discriminative Manifold
Distribution Alignment for Domain Adaptation," in IEEE Transactions on Systems, Man,
and Cybernetics: Systems, vol. 53, no. 2, pp. 1183-1197, Feb. 2023.