ARCHIVES
Original Article
An integrated DRB Net based deep learning model for Disease identification in Low-Resolution Potato Leaf Image
Rachit Khandelwal,1
Dr Paresh Jain2
1 2 Department of Electronics and Communication Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India.
Published Online: May-August 2026
Pages: 261-269
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502030
References
1. M. Radwan, A. A. Alhussan, A. Ibrahim, and S. M. Tawfeek, “Potato Leaf Disease Classification Using Optimized Machine Learning Models
and Feature Selection Techniques,” Potato Res., vol. 68, no. 2, pp. 897–921, Jun. 2025, doi: 10.1007/s11540-024-09763-8.
2. U. Hairah, A. Septiarini, N. Puspitasari, A. Tejawati, H. Hamdani, and S. Eka Priyatna, “Classification of tea leaf disease using convolutional
neural network approach,” Int. J. Electr. Comput. Eng. IJECE, vol. 14, no. 3, p. 3287, Jun. 2024, doi: 10.11591/ijece.v14i3.pp3287-3294.
3. N. Chaibi, W. Nhidi, and M. Zaied, “Mish-CNN: An Enhanced Deep Learning Model with Mish Activation Function for Potato Leaf Disease
Detection,” Potato Res., vol. 68, no. 3, pp. 3227–3236, Sep. 2025, doi: 10.1007/s11540-025-09877-7.
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5. T. Nazir, M. M. Iqbal, S. Jabbar, A. Hussain, and M. Albathan, “EfficientPNet—An Optimized and Efficient Deep Learning Approach for
Classifying Disease of Potato Plant Leaves,” Agriculture, vol. 13, no. 4, p. 841, Apr. 2023, doi: 10.3390/agriculture13040841.
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Multimed. Tools Appl., vol. 81, no. 15, pp. 20797–20816, Jun. 2022, doi: 10.1007/s11042-022-12620-w.
7. G. Al-Kateb, M. M. Mijwil, M. Aljanabi, M. Abotaleb, S. R. K. Priya, and P. Mishra, “AI-PotatoGuard: Leveraging Generative Models for
Early Detection of Potato Diseases,” Potato Res., vol. 68, no. 1, pp. 449–463, Mar. 2025, doi: 10.1007/s11540-024-09751-y.
8. Kumar A & Patel V K, “Classification and identification of disease in potato leaf using hierarchical based deep learning convolutional neural
network | Multimedia Tools and Applications.” Accessed: Aug. 25, 2025. [Online]. Available:
https://link.springer.com/article/10.1007/s11042-023-14663-z
9. A. M. Elshewey, S. M. Tawfeek, A. A. Alhussan, M. Radwan, and A. H. Abed, “Optimized Deep Learning for Potato BlightDetection Using
the Waterwheel Plant Algorithm and Sine Cosine Algorithm,” Potato Res., vol. 68, no. 1, pp. 1–25, Mar. 2025, doi: 10.1007/s11540-024-
09735-y.
10. E.-S. M. El-Kenawy et al., “Optimizing Potato Disease Classification Using a Metaheuristics Algorithm for Deep Learning: A Novel
Approach for Sustainable Agriculture,” Potato Res., vol. 68, no. 1, pp. 551–585, Mar. 2025, doi: 10.1007/s11540-024-09755-8.
11. S. A. Alzakari, A. A. Alhussan, A.-S. T. Qenawy, and A. M. Elshewey, “Early Detection of Potato Disease Using an Enhanced Convolutional
Neural Network-Long Short-Term Memory Deep Learning Model,” Potato Res., vol. 68, no. 1, pp. 695–713, Mar. 2025, doi: 10.1007/s11540-
024-09760-x.
12. P. Jha, D. Dembla, and W. Dubey, “Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for
Detection of Vegetable Crops Disease,” Int. J. Adv. Comput. Sci. Appl. IJACSA, vol. 15, no. 1, Jan. 2024, doi:
10.14569/IJACSA.2024.0150157.
13. H. Catal Reis and V. Turk, “Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and
transformer networks,” Eng. Appl. Artif. Intell., vol. 133, p. 108307, Jul. 2024, doi: 10.1016/j.engappai.2024.108307.
14. A. Bajpai, S. Sahu, and N. K. Tiwari, “Integrating Attention Mechanisms and Squeeze-and-Excitation Blocks for Accurate Potato Leaf
Disease Detection,” Potato Res., vol. 68, no. 3, pp. 2711–2731, Sep. 2025, doi: 10.1007/s11540-025-09847-z.
15. H. Paul et al., “A study and comparison of deep learning based potato leaf disease detection and classification techniques using explainable
AI,” Multimed. Tools Appl., vol. 83, no. 14, pp. 42485–42518, Apr. 2024, doi: 10.1007/s11042-023-17235-3.
16. Y. Xu, Z. Gao, J. Wang, Y. Zhou, J. Li, and X. Meng, “A Two-Stage Approach to the Study of Potato Disease Severity Classification,”
Agriculture, vol. 14, no. 3, p. 386, Feb. 2024, doi: 10.3390/agriculture14030386.
17. P. Mhala, A. Bilandani, and S. Sharma, “Enhancing crop productivity with fined-tuned deep convolution neural network for Potato leaf
disease detection,” Expert Syst. Appl., vol. 267, p. 126066, Apr. 2025, doi: 10.1016/j.eswa.2024.126066.
18. P. V. Yeswanth and S. Deivalakshmi, “ASFESRN: bridging the gap in real-time corn leaf disease detection with image super-resolution,”Multimed. Syst., vol. 30, no. 4, p. 175, Jun. 2024, doi: 10.1007/s00530-024-01377-x.
19. V. Thambawita, I. Strümke, S. A. Hicks, P. Halvorsen, S. Parasa, and M. A. Riegler, “Impact of Image Resolution on Deep Learning
Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images,” Diagn. Basel Switz.,
vol. 11, no. 12, p. 2183, Nov. 2021, doi: 10.3390/diagnostics11122183.
20. C. Dong, C. C. Loy, K. He, and X. Tang, “Image Super-Resolution Using Deep Convolutional Networks,” Jul. 31, 2015, arXiv:
arXiv:1501.00092. doi: 10.48550/arXiv.1501.00092.
21. C. Dong, C. C. Loy, and X. Tang, “Accelerating the Super-Resolution Convolutional Neural Network,” in Computer Vision – ECCV 2016,
B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., Cham: Springer International Publishing, 2016, pp. 391–407. doi: 10.1007/978-3-319-
46475-6_25.
22. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 2261–2269. doi: 10.1109/CVPR.2017.243.
23. Y. Tai, J. Yang, and X. Liu, “Image Super-Resolution via Deep Recursive Residual Network,” in 2017 IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 2790–2798. doi: 10.1109/CVPR.2017.298.
24. N. Ahn, B. Kang, and K.-A. Sohn, “Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network,” in Computer
Vision – ECCV 2018, vol. 11214, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., in Lecture Notes in Computer Science, vol.
11214. , Cham: Springer International Publishing, 2018, pp. 256–272. doi: 10.1007/978-3-030-01249-6_16.
25. Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual Dense Network for Image Super-Resolution,” in 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, Salt Lake City, UT: IEEE, Jun. 2018, pp. 2472–2481. doi: 10.1109/CVPR.2018.00262.
26. D. P. Hughes and M. Salathe, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,”
Apr. 12, 2016, arXiv: arXiv:1511.08060. doi: 10.48550/arXiv.1511.08060.
27. C. Hung, G. Kreiman, T. Poggio, and J. DiCarlo, “Fast readout of object identity from macaque,” science, vol. 1117593, no. 863, p. 310,
2005.
28. E. Elizar, M. A. Zulkifley, R. Muharar, M. H. M. Zaman, and S. M. Mustaza, “A Review on Multiscale-Deep-Learning Applications,”
Sensors, vol. 22, no. 19, p. 7384, Sep. 2022, doi: 10.3390/s22197384.
29. X. Li, W. Wang, X. Hu, and J. Yang, “Selective Kernel Networks,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), Long Beach, CA, USA: IEEE, Jun. 2019, pp. 510–519. doi: 10.1109/CVPR.2019.00060.
30. Y. Wang, Y. Li, G. Wang, and X. Liu, “Multi-scale Attention Network for Single Image Super-Resolution,” in 2024 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA: IEEE, Jun. 2024, pp. 5950–5960. doi:
10.1109/CVPRW63382.2024.00602.
31. B. Li, X. Wang, and H. Xu, “KSSANet: KAN-Driven Spatial-Spectral Attention Networks for Hyperspectral Image Super-Resolution,” in
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2025, pp. 1–5. doi:
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32. A. Khorramifar, M. Rasekh, H. Karami, U. Malaga-Toboła, and M. Gancarz, “A Machine Learning Method for Classification and
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10.3390/s21175836.
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doi: 10.48550/arXiv.2107.03055.
34. Yeswanth et al., “Residual Skip Network-Based Super-Resolution for Leaf Disease Detection of Grape Plant | Circuits, Systems, and Signal
Processing.” Accessed: Jul. 21, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s00034-023-02430-2
35. C. Tian, M. Song, X. Fan, X. Zheng, B. Zhang, and D. Zhang, “A Tree-guided CNN for image super-resolution,” 2025, arXiv. doi:
10.48550/ARXIV.2506.02585.
36. Yeswanth, P. V., and S. Deivalakshmi., “Extended wavelet sparse convolutional neural network (EWSCNN) for super resolution | Sādhanā.”
Accessed: Aug. 25, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s12046-023-02108-0
37. M. Haris, G. Shakhnarovich, and N. Ukita, “Deep Back-Projection Networks for Super-Resolution,” presented at the Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1664–1673. Accessed: Aug. 25, 2025. [Online]. Available:
https://openaccess.thecvf.com/content_cvpr_2018/html/Haris_Deep_Back-Projection_Networks_CVPR_2018_paper.html
38. C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a Deep Convolutional Network for Image Super-Resolution,” in Computer Vision –
ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds., Cham: Springer International Publishing, 2014, pp. 184–199. doi:
10.1007/978-3-319-10593-2_13.
39. N. Zhang, E. Zhang, G. Qi, F. Li, and C. Lv, “Lightweight grape leaf disease recognition method based on transformer framework,” Sci.
Rep., vol. 15, no. 1, p. 28974, Aug. 2025, doi: 10.1038/s41598-025-13689-7.
40. D. Qin et al., “MobileNetV4 -- Universal Models for the Mobile Ecosystem,” Sep. 29, 2024, arXiv: arXiv:2404.10518. doi:
10.48550/arXiv.2404.10518.
41. M.-H. Guo, Z.-N. Liu, T.-J. Mu, and S.-M. Hu, “Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 5, pp. 5436–5447, May 2023, doi: 10.1109/TPAMI.2022.3211006.
42. H. Liu, Z. Dai, D. So, and Q. V. Le, “Pay Attention to MLPs,” in Advances in Neural Information Processing Systems, Curran Associates,
Inc., 2021, pp. 9204–9215. Accessed: Aug. 25, 2025. [Online]. Available:
https://proceedings.neurips.cc/paper/2021/hash/4cc05b35c2f937c5bd9e7d41d3686fff-Abstract.html
43. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Apr. 10, 2015, arXiv:
arXiv:1409.1556. doi: 10.48550/arXiv.1409.1556.
and Feature Selection Techniques,” Potato Res., vol. 68, no. 2, pp. 897–921, Jun. 2025, doi: 10.1007/s11540-024-09763-8.
2. U. Hairah, A. Septiarini, N. Puspitasari, A. Tejawati, H. Hamdani, and S. Eka Priyatna, “Classification of tea leaf disease using convolutional
neural network approach,” Int. J. Electr. Comput. Eng. IJECE, vol. 14, no. 3, p. 3287, Jun. 2024, doi: 10.11591/ijece.v14i3.pp3287-3294.
3. N. Chaibi, W. Nhidi, and M. Zaied, “Mish-CNN: An Enhanced Deep Learning Model with Mish Activation Function for Potato Leaf Disease
Detection,” Potato Res., vol. 68, no. 3, pp. 3227–3236, Sep. 2025, doi: 10.1007/s11540-025-09877-7.
4. R. Mahum et al., “A novel framework for potato leaf disease detection using an efficient deep learning model,” Hum. Ecol. Risk Assess. Int.
J., vol. 29, no. 2, pp. 303–326, Feb. 2023, doi: 10.1080/10807039.2022.2064814.
5. T. Nazir, M. M. Iqbal, S. Jabbar, A. Hussain, and M. Albathan, “EfficientPNet—An Optimized and Efficient Deep Learning Approach for
Classifying Disease of Potato Plant Leaves,” Agriculture, vol. 13, no. 4, p. 841, Apr. 2023, doi: 10.3390/agriculture13040841.
6. W. Chen, J. Chen, A. Zeb, S. Yang, and D. Zhang, “Mobile convolution neural network for the recognition of potato leaf disease images,”
Multimed. Tools Appl., vol. 81, no. 15, pp. 20797–20816, Jun. 2022, doi: 10.1007/s11042-022-12620-w.
7. G. Al-Kateb, M. M. Mijwil, M. Aljanabi, M. Abotaleb, S. R. K. Priya, and P. Mishra, “AI-PotatoGuard: Leveraging Generative Models for
Early Detection of Potato Diseases,” Potato Res., vol. 68, no. 1, pp. 449–463, Mar. 2025, doi: 10.1007/s11540-024-09751-y.
8. Kumar A & Patel V K, “Classification and identification of disease in potato leaf using hierarchical based deep learning convolutional neural
network | Multimedia Tools and Applications.” Accessed: Aug. 25, 2025. [Online]. Available:
https://link.springer.com/article/10.1007/s11042-023-14663-z
9. A. M. Elshewey, S. M. Tawfeek, A. A. Alhussan, M. Radwan, and A. H. Abed, “Optimized Deep Learning for Potato BlightDetection Using
the Waterwheel Plant Algorithm and Sine Cosine Algorithm,” Potato Res., vol. 68, no. 1, pp. 1–25, Mar. 2025, doi: 10.1007/s11540-024-
09735-y.
10. E.-S. M. El-Kenawy et al., “Optimizing Potato Disease Classification Using a Metaheuristics Algorithm for Deep Learning: A Novel
Approach for Sustainable Agriculture,” Potato Res., vol. 68, no. 1, pp. 551–585, Mar. 2025, doi: 10.1007/s11540-024-09755-8.
11. S. A. Alzakari, A. A. Alhussan, A.-S. T. Qenawy, and A. M. Elshewey, “Early Detection of Potato Disease Using an Enhanced Convolutional
Neural Network-Long Short-Term Memory Deep Learning Model,” Potato Res., vol. 68, no. 1, pp. 695–713, Mar. 2025, doi: 10.1007/s11540-
024-09760-x.
12. P. Jha, D. Dembla, and W. Dubey, “Implementation of Machine Learning Classification Algorithm Based on Ensemble Learning for
Detection of Vegetable Crops Disease,” Int. J. Adv. Comput. Sci. Appl. IJACSA, vol. 15, no. 1, Jan. 2024, doi:
10.14569/IJACSA.2024.0150157.
13. H. Catal Reis and V. Turk, “Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and
transformer networks,” Eng. Appl. Artif. Intell., vol. 133, p. 108307, Jul. 2024, doi: 10.1016/j.engappai.2024.108307.
14. A. Bajpai, S. Sahu, and N. K. Tiwari, “Integrating Attention Mechanisms and Squeeze-and-Excitation Blocks for Accurate Potato Leaf
Disease Detection,” Potato Res., vol. 68, no. 3, pp. 2711–2731, Sep. 2025, doi: 10.1007/s11540-025-09847-z.
15. H. Paul et al., “A study and comparison of deep learning based potato leaf disease detection and classification techniques using explainable
AI,” Multimed. Tools Appl., vol. 83, no. 14, pp. 42485–42518, Apr. 2024, doi: 10.1007/s11042-023-17235-3.
16. Y. Xu, Z. Gao, J. Wang, Y. Zhou, J. Li, and X. Meng, “A Two-Stage Approach to the Study of Potato Disease Severity Classification,”
Agriculture, vol. 14, no. 3, p. 386, Feb. 2024, doi: 10.3390/agriculture14030386.
17. P. Mhala, A. Bilandani, and S. Sharma, “Enhancing crop productivity with fined-tuned deep convolution neural network for Potato leaf
disease detection,” Expert Syst. Appl., vol. 267, p. 126066, Apr. 2025, doi: 10.1016/j.eswa.2024.126066.
18. P. V. Yeswanth and S. Deivalakshmi, “ASFESRN: bridging the gap in real-time corn leaf disease detection with image super-resolution,”Multimed. Syst., vol. 30, no. 4, p. 175, Jun. 2024, doi: 10.1007/s00530-024-01377-x.
19. V. Thambawita, I. Strümke, S. A. Hicks, P. Halvorsen, S. Parasa, and M. A. Riegler, “Impact of Image Resolution on Deep Learning
Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images,” Diagn. Basel Switz.,
vol. 11, no. 12, p. 2183, Nov. 2021, doi: 10.3390/diagnostics11122183.
20. C. Dong, C. C. Loy, K. He, and X. Tang, “Image Super-Resolution Using Deep Convolutional Networks,” Jul. 31, 2015, arXiv:
arXiv:1501.00092. doi: 10.48550/arXiv.1501.00092.
21. C. Dong, C. C. Loy, and X. Tang, “Accelerating the Super-Resolution Convolutional Neural Network,” in Computer Vision – ECCV 2016,
B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., Cham: Springer International Publishing, 2016, pp. 391–407. doi: 10.1007/978-3-319-
46475-6_25.
22. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 2261–2269. doi: 10.1109/CVPR.2017.243.
23. Y. Tai, J. Yang, and X. Liu, “Image Super-Resolution via Deep Recursive Residual Network,” in 2017 IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 2790–2798. doi: 10.1109/CVPR.2017.298.
24. N. Ahn, B. Kang, and K.-A. Sohn, “Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network,” in Computer
Vision – ECCV 2018, vol. 11214, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., in Lecture Notes in Computer Science, vol.
11214. , Cham: Springer International Publishing, 2018, pp. 256–272. doi: 10.1007/978-3-030-01249-6_16.
25. Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual Dense Network for Image Super-Resolution,” in 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, Salt Lake City, UT: IEEE, Jun. 2018, pp. 2472–2481. doi: 10.1109/CVPR.2018.00262.
26. D. P. Hughes and M. Salathe, “An open access repository of images on plant health to enable the development of mobile disease diagnostics,”
Apr. 12, 2016, arXiv: arXiv:1511.08060. doi: 10.48550/arXiv.1511.08060.
27. C. Hung, G. Kreiman, T. Poggio, and J. DiCarlo, “Fast readout of object identity from macaque,” science, vol. 1117593, no. 863, p. 310,
2005.
28. E. Elizar, M. A. Zulkifley, R. Muharar, M. H. M. Zaman, and S. M. Mustaza, “A Review on Multiscale-Deep-Learning Applications,”
Sensors, vol. 22, no. 19, p. 7384, Sep. 2022, doi: 10.3390/s22197384.
29. X. Li, W. Wang, X. Hu, and J. Yang, “Selective Kernel Networks,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), Long Beach, CA, USA: IEEE, Jun. 2019, pp. 510–519. doi: 10.1109/CVPR.2019.00060.
30. Y. Wang, Y. Li, G. Wang, and X. Liu, “Multi-scale Attention Network for Single Image Super-Resolution,” in 2024 IEEE/CVF Conference
on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA: IEEE, Jun. 2024, pp. 5950–5960. doi:
10.1109/CVPRW63382.2024.00602.
31. B. Li, X. Wang, and H. Xu, “KSSANet: KAN-Driven Spatial-Spectral Attention Networks for Hyperspectral Image Super-Resolution,” in
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr. 2025, pp. 1–5. doi:
10.1109/ICASSP49660.2025.10890177.
32. A. Khorramifar, M. Rasekh, H. Karami, U. Malaga-Toboła, and M. Gancarz, “A Machine Learning Method for Classification and
Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array,” Sensors, vol. 21, no. 17, p. 5836, Aug. 2021, doi:
10.3390/s21175836.
33. A. Liu, Y. Liu, J. Gu, Y. Qiao, and C. Dong, “Blind Image Super-Resolution: A Survey and Beyond,” Jul. 07, 2021, arXiv: arXiv:2107.03055.
doi: 10.48550/arXiv.2107.03055.
34. Yeswanth et al., “Residual Skip Network-Based Super-Resolution for Leaf Disease Detection of Grape Plant | Circuits, Systems, and Signal
Processing.” Accessed: Jul. 21, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s00034-023-02430-2
35. C. Tian, M. Song, X. Fan, X. Zheng, B. Zhang, and D. Zhang, “A Tree-guided CNN for image super-resolution,” 2025, arXiv. doi:
10.48550/ARXIV.2506.02585.
36. Yeswanth, P. V., and S. Deivalakshmi., “Extended wavelet sparse convolutional neural network (EWSCNN) for super resolution | Sādhanā.”
Accessed: Aug. 25, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s12046-023-02108-0
37. M. Haris, G. Shakhnarovich, and N. Ukita, “Deep Back-Projection Networks for Super-Resolution,” presented at the Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1664–1673. Accessed: Aug. 25, 2025. [Online]. Available:
https://openaccess.thecvf.com/content_cvpr_2018/html/Haris_Deep_Back-Projection_Networks_CVPR_2018_paper.html
38. C. Dong, C. C. Loy, K. He, and X. Tang, “Learning a Deep Convolutional Network for Image Super-Resolution,” in Computer Vision –
ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds., Cham: Springer International Publishing, 2014, pp. 184–199. doi:
10.1007/978-3-319-10593-2_13.
39. N. Zhang, E. Zhang, G. Qi, F. Li, and C. Lv, “Lightweight grape leaf disease recognition method based on transformer framework,” Sci.
Rep., vol. 15, no. 1, p. 28974, Aug. 2025, doi: 10.1038/s41598-025-13689-7.
40. D. Qin et al., “MobileNetV4 -- Universal Models for the Mobile Ecosystem,” Sep. 29, 2024, arXiv: arXiv:2404.10518. doi:
10.48550/arXiv.2404.10518.
41. M.-H. Guo, Z.-N. Liu, T.-J. Mu, and S.-M. Hu, “Beyond Self-Attention: External Attention Using Two Linear Layers for Visual Tasks,”
IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 5, pp. 5436–5447, May 2023, doi: 10.1109/TPAMI.2022.3211006.
42. H. Liu, Z. Dai, D. So, and Q. V. Le, “Pay Attention to MLPs,” in Advances in Neural Information Processing Systems, Curran Associates,
Inc., 2021, pp. 9204–9215. Accessed: Aug. 25, 2025. [Online]. Available:
https://proceedings.neurips.cc/paper/2021/hash/4cc05b35c2f937c5bd9e7d41d3686fff-Abstract.html
43. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Apr. 10, 2015, arXiv:
arXiv:1409.1556. doi: 10.48550/arXiv.1409.1556.
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