Artificial Intelligence Based Low-Level Vision Techniques: Progress and Challenges
A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Computer Vision and Pattern Recognition".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 4
Special Issue Editors
Interests: low-level vision; deep learning; industrial anomaly detection
Interests: low-level vision; computational photography
Special Issue Information
Dear Colleagues,
The development of artificial intelligence has brought substantial new opportunities for low-level vision. On the one hand, the recent innovations of artificial intelligence have injected more possibilities into low-level vision. For example, novel neural network architectures such as Transformer and Spiking Neural Networks (SNNs) have largely extended the ability of low-level vision models. The rise of generative models, large models, and agentic models has introduced more effective priors and knowledge into low-level vision. The progress of three-dimensional (3D) vision and world model has also expanded the ability boundary of low-level vision. On the other hand, the development of artificial intelligence has also enabled new forms of imaging equipments, bringing more possible imaging methods or improving the imaging quality of existing sensors, such as event cameras, multispectral imaging, non-line-of-sight imaging, etc.
In order to explore and discuss the progress and challenges of artificial intelligence-based low-level vision techniques under the current research background, we have organized this Special Issue. We welcome discussions about low-level vision and imaging techniques empowered by artificial intelligence, including but not limited to novel network architectures, new technology routes, newly emerging imaging techniques, benchmarks, review papers, and ideas about future development routes of low-level vision.
Dr. Ming Liu
Dr. Zhilu Zhang
Dr. Yue Cao
Guest Editors
Manuscript Submission Information
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Keywords
- low-level vision
- artificial intelligence
- image signal processing
- imaging techniques
- imaging equipment
- neural networks
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