Deep Learning Algorithms for Computer Vision and Image Processing

Dear Colleagues,

In recent years, deep neural networks have shown remarkable performance in a wide range of computer vision and image processing tasks, outperforming traditional techniques by a large margin.

Computer vision and image processing applications have become increasingly complex and diverse, encompassing areas such as medical imaging, autonomous driving, industrial inspection, satellite imaging, and augmented reality . These domains demand robust, scalable, and intelligent algorithms capable of interpreting visual data with high accuracy and efficiency. Deep learning methods, particularly convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and self-supervised learning approaches, have proven to be highly effective in addressing these challenges.

The aim of this Special Issue is to bring together recent advancements and novel applications of deep learning in computer vision and image processing. We invite researchers and practitioners to contribute original research articles, comprehensive reviews, or innovative application papers that demonstrate the power and versatility of deep learning in solving real-world problems related to visual data processing.

Topics of interest include, but are not limited to, the following

1. Deep learning architectures for image and video processing tasks;
2. Image and video segmentation using deep networks;
3. Image restoration, enhancement, and super-resolution using deep learning;
4. Deep learning for medical image analysis and clinical applications;
5. 3D image and point cloud processing using deep models;
6. Generative models (GANs, diffusion models) for image synthesis and augmentation;
7. Transfer learning and self-supervised learning in vision tasks;
8. Real-time and resource-efficient deep learning algorithms for embedded systems;
9. Multimodal large language models for image and video analysis.

We welcome both theoretical and applied contributions that advance the state of the art in deep learning for visual understanding.

We look forward to your valuable contributions to this Special Issue.

Dr. Haoji Hu
Guest Editor

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