Emerging Deep Learning Models and Applications in Image Processing and Computer Vision
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 138
Special Issue Editor
Special Issue Information
Dear Colleagues,
The goal of this Special Issue is to gather articles that address new and existing challenges in image processing and computer vision using advanced deep learning algorithms and models. We are particularly interested in approaches that push the boundaries of traditional image analysis by leveraging innovative deep learning techniques. Studies that integrate hybrid methods, combining deep learning with other machine learning or optimization techniques, are also highly encouraged. Furthermore, applications that use novel deep neural network architectures and frameworks—such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models—for tasks like image recognition, object detection, segmentation, anomaly detection, 3D Gaussian splatting, and 3D image reconstruction, would be highly relevant to this Special Issue. We welcome a wide range of contributions, especially those demonstrating practical applications, theoretical advancements, or novel implementations in real-world computer vision tasks.
Dr. Kyeongbo Kong
Guest Editor
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Keywords
- image recognition
- object detection
- image segmentation
- image completion
- anomaly detection
- 3D Gaussian splatting
- 3D image reconstruction
- hybrid deep learning approaches
- real-time visual recognition
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