Deep Learning and Machine Learning for Image Processing: Algorithms and Applications
A special issue of Computers (ISSN 2073-431X).
Deadline for manuscript submissions: 28 February 2025 | Viewed by 156
Special Issue Editor
Interests: image analysis theory and methods (remote sensing, medicine, fiber, natural images, etc.); machine learning and pattern recognition (deep learning, tensor analysis, classification, segmentation, regression, etc.); intelligent system development and applications
Special Issue Information
Dear Colleagues,
Deep learning and machine learning have revolutionized the field of image processing, offering unprecedented levels of accuracy and efficiency in tasks such as object detection, image classification, image generation, and image segmentation. This Special Issue aims to bring together state-of-the-art research on deep learning and machine learning techniques for image processing, with a focus on algorithms and applications.
Topics of interest include, but are not limited to, the following:
- Deep learning models for image classification and object detection;
- Generative adversarial networks for image synthesis and editing;
- Machine learning approaches for image segmentation and localization;
- Transfer learning and domain adaptation for image processing;
- Fusion of deep learning and conventional techniques for image enhancement;
- Applications of deep learning and machine learning in medical imaging, remote sensing, computer vision, and more.
Authors are invited to submit original research articles, reviews, and short communications addressing the above topics. Extended conference papers are also welcome, but they should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases.
Dr. Zhao Chen
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- machine learning
- image processing
- computer vision
- feature extraction
- image classification
- object detection
- image segmentation
- generative adversarial networks
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