Research on image processing based on machine learning feature fusion and sparse representation

[1] Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G., & Willett, R. (2020). Deep learning techniques for inverse problems in imaging. IEEE Journal on Selected Areas in Information Theory, 1(1), 39-56. Ongie G. Jalal A. Metzler C. A. Baraniuk R. G. Dimakis A. G. Willett R. ( 2020 ). Deep learning techniques for inverse problems in imaging . IEEE Journal on Selected Areas in Information Theory , 1 ( 1 ), 39 - 56 . Search in Google Scholar

[2] Yuan, X., Shi, J., & Gu, L. (2021). A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Systems with Applications, 169, 114417. Yuan X. Shi J. Gu L. ( 2021 ). A review of deep learning methods for semantic segmentation of remote sensing imagery . Expert Systems with Applications , 169 , 114417 . Search in Google Scholar

[3] Poostchi, M., Silamut, K., Maude, R. J., Jaeger, S., & Thoma, G. (2018). Image analysis and machine learning for detecting malaria. Translational Research, 194, 36-55. Poostchi M. Silamut K. Maude R. J. Jaeger S. Thoma G. ( 2018 ). Image analysis and machine learning for detecting malaria . Translational Research , 194 , 36 - 55 . Search in Google Scholar

[4] Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., & Garcia-Rodriguez, J. (2018). A survey on deep learning techniques for image and video semantic segmentation. Applied Soft Computing, 70, 41-65. Garcia-Garcia A. Orts-Escolano S. Oprea S. Villena-Martinez V. Martinez-Gonzalez P. Garcia-Rodriguez J. ( 2018 ). A survey on deep learning techniques for image and video semantic segmentation . Applied Soft Computing , 70 , 41 - 65 . Search in Google Scholar

[5] Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9(1), 381-386. Mahesh B. ( 2020 ). Machine learning algorithms-a review . International Journal of Science and Research (IJSR).[Internet] , 9 ( 1 ), 381 - 386 . Search in Google Scholar

[6] Ray, S. (2019, February). A quick review of machine learning algorithms. In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35-39). IEEE. Ray S. ( 2019 , February ). A quick review of machine learning algorithms . In 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 35 - 39 ). IEEE . Search in Google Scholar

[7] Alpaydin, E. (2021). Machine learning. MIT press. Alpaydin E. ( 2021 ). Machine learning . MIT press . Search in Google Scholar

[8] Bonaccorso, G. (2018). Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd. Bonaccorso G. ( 2018 ). Machine Learning Algorithms: Popular algorithms for data science and machine learning . Packt Publishing Ltd . Search in Google Scholar

[9] Fatima, M., & Pasha, M. (2017). Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications, 9(01), 1-16. Fatima M. Pasha M. ( 2017 ). Survey of machine learning algorithms for disease diagnostic . Journal of Intelligent Learning Systems and Applications , 9 ( 01 ), 1 - 16 . Search in Google Scholar

[10] Shinde, P. P., & Shah, S. (2018, August). A review of machine learning and deep learning applications. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-6). IEEE. Shinde P. P. Shah S. ( 2018 , August ). A review of machine learning and deep learning applications . In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1 - 6 ). IEEE . Search in Google Scholar

[11] Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19(1), 221-248. Shen D. Wu G. Suk H. I. ( 2017 ). Deep learning in medical image analysis . Annual review of biomedical engineering , 19 ( 1 ), 221 - 248 . Search in Google Scholar

[12] Ker, J., Wang, L., Rao, J., & Lim, T. (2017). Deep learning applications in medical image analysis. Ieee Access, 6, 9375-9389. Ker J. Wang L. Rao J. Lim T. ( 2017 ). Deep learning applications in medical image analysis . Ieee Access , 6 , 9375 - 9389 . Search in Google Scholar

[13] Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. Classification in BioApps: Automation of decision making, 323-350. Razzak M. I. Naz S. Zaib A. ( 2018 ). Deep learning for medical image processing: Overview, challenges and the future . Classification in BioApps: Automation of decision making , 323 - 350 . Search in Google Scholar

[14] Fang, L., Wang, C., Li, S., & Benediktsson, J. A. (2017). Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Transactions on Instrumentation and Measurement, 66(7), 1646-1657. Fang L. Wang C. Li S. Benediktsson J. A. ( 2017 ). Hyperspectral image classification via multiple-feature-based adaptive sparse representation . IEEE Transactions on Instrumentation and Measurement , 66 ( 7 ), 1646 - 1657 . Search in Google Scholar

[15] Mei, Y., Fan, Y., & Zhou, Y. (2021). Image super-resolution with non-local sparse attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3517-3526). Mei Y. Fan Y. Zhou Y. ( 2021 ). Image super-resolution with non-local sparse attention . In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3517 - 3526 ). Search in Google Scholar

[16] Kanaan, H., & Behrad, A. (2020). Three-dimensional shape recognition and classification using local features of model views and sparse representation of shape descriptors. Journal of Information Processing Systems, 16(2), 343-359. Kanaan H. Behrad A. ( 2020 ). Three-dimensional shape recognition and classification using local features of model views and sparse representation of shape descriptors . Journal of Information Processing Systems , 16 ( 2 ), 343 - 359 . Search in Google Scholar

[17] Ortiz, A., Lozano, F., Gorriz, J. M., Ramirez, J., Martinez Murcia, F. J., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Discriminative sparse features for Alzheimer’s disease diagnosis using multimodal image data. Current Alzheimer Research, 15(1), 67-79. Ortiz A. Lozano F. Gorriz J. M. Ramirez J. Martinez Murcia F. J. Alzheimer’s Disease Neuroimaging Initiative ( 2018 ). Discriminative sparse features for Alzheimer’s disease diagnosis using multimodal image data . Current Alzheimer Research , 15 ( 1 ), 67 - 79 . Search in Google Scholar

[18] Liu, Y., Wang, L., Cheng, J., Li, C., & Chen, X. (2020). Multi-focus image fusion: A survey of the state of the art. Information Fusion, 64, 71-91. Liu Y. Wang L. Cheng J. Li C. Chen X. ( 2020 ). Multi-focus image fusion: A survey of the state of the art . Information Fusion , 64 , 71 - 91 . Search in Google Scholar

[19] Komura, D., & Ishikawa, S. (2018). Machine learning methods for histopathological image analysis. Computational and structural biotechnology journal, 16, 34-42. Komura D. Ishikawa S. ( 2018 ). Machine learning methods for histopathological image analysis . Computational and structural biotechnology journal , 16 , 34 - 42 . Search in Google Scholar

[20] Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C. W. (2020). Deep learning on image denoising: An overview. Neural Networks, 131, 251-275. Tian C. Fei L. Zheng W. Xu Y. Zuo W. Lin C. W. ( 2020 ). Deep learning on image denoising: An overview . Neural Networks , 131 , 251 - 275 . Search in Google Scholar

[21] Wang, P., Fan, E., & Wang, P. (2021). Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern recognition letters, 141, 61-67. Wang P. Fan E. Wang P. ( 2021 ). Comparative analysis of image classification algorithms based on traditional machine learning and deep learning . Pattern recognition letters , 141 , 61 - 67 . Search in Google Scholar

[22] Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., … & Nahavandi, S. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information fusion, 76, 243-297. Abdar M. Pourpanah F. Hussain S. Rezazadegan D. Liu L. Ghavamzadeh M. Nahavandi S. ( 2021 ). A review of uncertainty quantification in deep learning: Techniques, applications and challenges . Information fusion , 76 , 243 - 297 . Search in Google Scholar

[23] Jasim, M. A., & Al-Tuwaijari, J. M. (2020, April). Plant leaf diseases detection and classification using image processing and deep learning techniques. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 259-265). IEEE. Jasim M. A. Al-Tuwaijari J. M. ( 2020 , April ). Plant leaf diseases detection and classification using image processing and deep learning techniques . In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 259 - 265 ). IEEE . Search in Google Scholar

[24] Hesamian, M. H., Jia, W., He, X., & Kennedy, P. (2019). Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging, 32, 582-596. Hesamian M. H. Jia W. He X. Kennedy P. ( 2019 ). Deep learning techniques for medical image segmentation: achievements and challenges . Journal of digital imaging , 32 , 582 - 596 . Search in Google Scholar

[25] Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160. Sarker I. H. ( 2021 ). Machine learning: Algorithms, real-world applications and research directions . SN computer science , 2 ( 3 ), 160 . Search in Google Scholar

[26] Zhang, Q., Liu, Y., Blum, R. S., Han, J., & Tao, D. (2018). Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review. Information Fusion, 40, 57-75. Zhang Q. Liu Y. Blum R. S. Han J. Tao D. ( 2018 ). Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review . Information Fusion , 40 , 57 - 75 . Search in Google Scholar

[27] Zhu, Z., Yin, H., Chai, Y., Li, Y., & Qi, G. (2018). A novel multi-modality image fusion method based on image decomposition and sparse representation. Information Sciences, 432, 516-529. Zhu Z. Yin H. Chai Y. Li Y. Qi G. ( 2018 ). A novel multi-modality image fusion method based on image decomposition and sparse representation . Information Sciences , 432 , 516 - 529 . Search in Google Scholar

[28] Gu, S., Meng, D., Zuo, W., & Zhang, L. (2017). Joint convolutional analysis and synthesis sparse representation for single image layer separation. In Proceedings of the IEEE international conference on computer vision (pp. 1708-1716). Gu S. Meng D. Zuo W. Zhang L. ( 2017 ). Joint convolutional analysis and synthesis sparse representation for single image layer separation . In Proceedings of the IEEE international conference on computer vision (pp. 1708 - 1716 ). Search in Google Scholar

[29] Zhuang, L., & Bioucas-Dias, J. M. (2018). Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3), 730-742. Zhuang L. Bioucas-Dias J. M. ( 2018 ). Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 11 ( 3 ), 730 - 742 . Search in Google Scholar

[30] Liu, Y., Chen, X., Ward, R. K., & Wang, Z. J. (2019). Medical image fusion via convolutional sparsity based morphological component analysis. IEEE Signal Processing Letters, 26(3), 485-489. Liu Y. Chen X. Ward R. K. Wang Z. J. ( 2019 ). Medical image fusion via convolutional sparsity based morphological component analysis . IEEE Signal Processing Letters , 26 ( 3 ), 485 - 489 . Search in Google Scholar

[31] Li, S., Dian, R., Fang, L., & Bioucas-Dias, J. M. (2018). Fusing hyperspectral and multispectral images via coupled sparse tensor factorization. IEEE Transactions on Image Processing, 27(8), 4118-4130. Li S. Dian R. Fang L. Bioucas-Dias J. M. ( 2018 ). Fusing hyperspectral and multispectral images via coupled sparse tensor factorization . IEEE Transactions on Image Processing , 27 ( 8 ), 4118 - 4130 . Search in Google Scholar

[32] Farnaz Hoseini, Shohreh Shamlou & Milad Ahmadi Gharehtoragh. (2024). Segmentation of MR images for brain tumor detection using autoencoder neural network. Discover Artificial Intelligence(1),71-71. Hoseini Farnaz Shamlou Shohreh Gharehtoragh Milad Ahmadi ( 2024 ). Segmentation of MR images for brain tumor detection using autoencoder neural network . Discover Artificial Intelligence ( 1 ), 71 - 71 . Search in Google Scholar

[33] Rehan Zubair Khalid, Ibrahim Ahmed, Atta Ullah, Enrico Zio & Asifullah Khan. (2024). Enhancing accuracy of prediction of critical heat flux in Circular channels by ensemble of deep sparse autoencoders and deep neural Networks. Nuclear Engineering and Design113587-113587. Khalid Rehan Zubair Ahmed Ibrahim Ullah Atta Zio Enrico Khan Asifullah ( 2024 ). Enhancing accuracy of prediction of critical heat flux in Circular channels by ensemble of deep sparse autoencoders and deep neural Networks . Nuclear Engineering and Design 113587 - 113587 . Search in Google Scholar

[34] Fengmiao Bian, Jian Feng Cai & Rui Zhang. (2024). A Preconditioned Riemannian Gradient Descent Algorithm for Low-Rank Matrix Recovery. SIAM Journal on Matrix Analysis and Applications(4),2075-2103. Bian Fengmiao Cai Jian Feng Zhang Rui ( 2024 ). A Preconditioned Riemannian Gradient Descent Algorithm for Low-Rank Matrix Recovery . SIAM Journal on Matrix Analysis and Applications ( 4 ), 2075 - 2103 . Search in Google Scholar

[35] Hui Yicong, Zhang Yanchao, Tang Jie,Li Zhe, Chen Runlin & Cui Yahui. (2024). Clustering-based regularized orthogonal matching pursuit algorithm for rolling element bearing fault diagnosis. Transactions of the Institute of Measurement and Control(14),2795-2803. Yicong Hui Yanchao Zhang Jie Tang Zhe Li Runlin Chen Yahui Cui ( 2024 ). Clustering-based regularized orthogonal matching pursuit algorithm for rolling element bearing fault diagnosis . Transactions of the Institute of Measurement and Control ( 14 ), 2795 - 2803 . Search in Google Scholar

[36] Huang Peike, Sun Jie, Qin Xinghao & Li Jixun. (2024). A novel adaptive super-twisting trajectory tracking control with back propagation algorithm for a quadrotor UAV. Proceedings of the Institution of Mechanical Engineers(9),1625-1639. Peike Huang Jie Sun Xinghao Qin Jixun Li ( 2024 ). A novel adaptive super-twisting trajectory tracking control with back propagation algorithm for a quadrotor UAV . Proceedings of the Institution of Mechanical Engineers ( 9 ), 1625 - 1639 . Search in Google Scholar

[37] Wolfgang Mader, Yannick Linke, Malenka Mader, Linda Sommerlade, Jens Timmer & Björn Schelter. (2014). A numerically efficient implementation of the expectation maximization algorithm for state space models. Applied Mathematics and Computation222-232. Mader Wolfgang Linke Yannick Mader Malenka Sommerlade Linda Timmer Jens Schelter Björn ( 2014 ). A numerically efficient implementation of the expectation maximization algorithm for state space models . Applied Mathematics and Computation 222 - 232 . Search in Google Scholar