Overview of Deep Learning Image Processing

In the last decade, deep learning has rapidly advanced and achieved significant breakthroughs in the field of image processing. This survey aims to provide a comprehensive overview of recent advancements in deep learning-based image processing technology. Initially, it introduces the fundamental principles of deep learning and commonly used network architectures, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and the ELM-RVFL series. These networks have found widespread applications in image processing tasks. Subsequently, the survey examines the current state of deep learning in image classification, object detection, and image segmentation, where deep learning models have demonstrated exceptional performance, surpassing traditional methods in terms of accuracy and efficiency. Furthermore, it explores various applications of deep learning in areas such as face recognition, medical imaging, and pedestrian detection, leading to breakthroughs in accuracy and robustness. Finally, the survey explores the symbiotic relationship between deep learning and image processing, highlighting potential future directions for new deep models and training methods. These insights aim to inspire researchers to explore innovative approaches in deep learning for image processing, ultimately advancing image processing technology and its diverse applications.