Editorial on the Special Issue: New Trends in Image Processing III
Source abstract
Abstract
The image processing field is undergoing a significant transformation owing to rapid advancements in deep learning, computer vision, and artificial intelligence [...]
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Advanced Neural Network Applications · Image and Signal Denoising Methods · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Centre for Visual Computing, University of Bradford, Bradford BD7 1DP, UK
References (42)
Show source-provided references
1. Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification10.1016/j.isprsjprs.2023.01.014 ↗
2. Continual Object Detection: A review of definitions, strategies, and challenges10.1016/j.neunet.2023.01.041 ↗
3. Deep Learning Based Object Detection for Resource Constrained Devices-Systematic Review, Future Trends and Challenges Ahead10.1016/j.neucom.2023.02.006 ↗
4. An improved fire detection approach based on YOLO-v8 for smart cities10.1007/s00521-023-08809-1 ↗
5. Lu, Y., Jiang, Q., Chen, R., Hou, Y., Zhu, X., and Ma, Y. (2023, January 2–6). See more and know more: Zero-shot point cloud segmentation via multi-modal visual data. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France.10.1109/ICCV51070.2023.01981 ↗
6. Image super-resolution: A comprehensive review, recent trends, challenges and applications10.1016/j.inffus.2022.10.007 ↗
7. Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification10.3390/math11092008 ↗
8. Tan, R., Yuan, Y., Huang, R., and Luo, J. (2022, January 18–22). Video super-resolution with spatial-temporal transformer encoder. Proceedings of the 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan.10.1109/ICME52920.2022.9859774 ↗
9. Srdiff: Single image super-resolution with diffusion probabilistic models10.1016/j.neucom.2022.01.029 ↗
10. Li, H., and Zhang, P. (2021, January 18–22). Spatio-temporal fusion network for video super-resolution. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.10.1109/IJCNN52387.2021.9533610 ↗
11. Karlinsky, L., Michaeli, T., and Nishino, K. (2023). Computer Vision—ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, Springer.10.1007/978-3-031-25056-9 ↗
12. Lepcha, D.C., and Goyal, B. (2021, January 3–4). Medical-modality super-resolution for increased visualisation of intracranial tissue details and structural details. Proceedings of the 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.10.1109/ICRITO51393.2021.9596440 ↗
13. An efficient medical image super resolution based on piecewise linear regression strategy using domain transform filtering10.1002/cpe.6644 ↗
14. Qiu, D., Cheng, Y., and Wang, X. (2023). Medical image super-resolution reconstruction algorithms based on deep learning: A survey. Comput. Methods Programs Biomed., 238.10.1016/j.cmpb.2023.107590 ↗
15. NasmamSR: A fast image super-resolution network based on neural architecture search and multiple attention mechanism10.1007/s00530-021-00841-2 ↗
16. Wang, X., Sun, L., Chehri, A., and Song, Y. (2023). A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images. Remote Sens., 15.10.3390/rs15205062 ↗
17. Enhanced resolution of FY4 remote sensing visible spectrum images utilizing super-resolution and transfer learning techniques10.1109/JSTARS.2022.3197401 ↗
18. Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes10.1038/s41587-022-01471-3 ↗
19. Super-resolution imaging of non-fluorescent molecules by photothermal relaxation localization microscopy10.1038/s41566-022-01143-3 ↗
20. Filiot, A., Ghermi, R., Olivier, A., Jacob, P., Fidon, L., Mac Kain, A., Saillard, C., and Schiratti, J.-B. (2023). Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling. medRxiv.10.1101/2023.07.21.23292757 ↗
21. Jamil, S., Jalil Piran, M., and Kwon, O.-J. (2023). A comprehensive survey of transformers for computer vision. Drones, 7.10.2139/ssrn.4332114 ↗
22. Benchmarking and scaling of deep learning models for land cover image classification10.1016/j.isprsjprs.2022.11.012 ↗
23. Novack, Z., McAuley, J., Lipton, Z.C., and Garg, S. (2023, January 23–29). Chils: Zero-shot image classification with hierarchical label sets. Proceedings of the International Conference on Machine Learning, Honolulu, HI, USA.
24. Guidelines and evaluation of clinical explainable AI in medical image analysis10.1016/j.media.2022.102684 ↗
25. Features kept generative adversarial network data augmentation strategy for hyperspectral image classification10.1016/j.patcog.2023.109701 ↗
26. Deep hybrid: Multi-graph neural network collaboration for hyperspectral image classification10.1016/j.dt.2022.02.007 ↗
27. Lee, K., Kim, S., and Lee, E.C. (2023). Fast and Accurate Facial Expression Image Classification and Regression Method Based on Knowledge Distillation. Appl. Sci., 13.10.3390/app13116409 ↗
28. Liu, S., Zha, J., Sun, J., Li, Z., and Wang, G. (2023). EdgeYOLO: An Edge-Real-Time Object Detector. arXiv.10.23919/CCC58697.2023.10239786 ↗
29. Privacy-Preserving Federated Learning of Remote Sensing Image Classification with Dishonest-Majority10.1109/JSTARS.2023.3276781 ↗
30. Ahn, H., Son, S., Roh, J., Baek, H., Lee, S., Chung, Y., and Park, D. (2023). SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning. Appl. Sci., 13.10.3390/app132011237 ↗
31. PHM-IRNET: Self-training thermal segmentation approach for thermographic inspection of industrial components10.1016/j.ndteint.2023.102884 ↗
32. An overview of intelligent image segmentation using active contour models10.20517/ir.2023.02 ↗
33. Stancato, G., and Piga, B.E.A. (2023). Beyond Digital Representation: Advanced Experiences in AR and AI for Cultural Heritage and Innovative Design, Springer.
34. Number plate recognition from enhanced super-resolution using generative adversarial network10.1007/s11042-022-14018-0 ↗
35. Jiang, J., Chen, X., Tian, G., and Liu, Y. (2023). ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation. arXiv.10.1109/ISBI53787.2023.10230496 ↗
36. Huang, M., Dai, W., Yan, W., and Wang, J. (2023). High-Resolution Remote Sensing Image Segmentation Algorithm Based on Improved Feature Extraction and Hybrid Attention Mechanism. Electronics, 12.10.3390/electronics12173660 ↗
37. Multi-ConDoS: Multimodal contrastive domain sharing generative adversarial networks for self-supervised medical image segmentation
38. Xu, G., Wang, S., Lukasiewicz, T., and Xu, Z. (2023, January 10–14). Adaptive-Masking Policy with Deep Reinforcement Learning for Self-Supervised Medical Image Segmentation. Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME), Brisbane, Australia.10.1109/ICME55011.2023.00390 ↗
39. Transformer models used for text-based question answering systems10.1007/s10489-022-04052-8 ↗
40. Multiscale lightweight 3D segmentation algorithm with attention mechanism: Brain tumor image segmentation10.1016/j.eswa.2022.119166 ↗
41. Mia, M.S., Arnob, A.B.H., Naim+, A., Voban, A.A.B., and Islam, M.S. (2023). ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain. arXiv.10.1109/ICCD59681.2023.10420683 ↗
42. Visual attention network10.1007/s41095-023-0364-2 ↗