Lecturer at Wuhan University of Science and Technology, China
Zheng chaobing is a lecturer at the School of Information Science and Engineering, Wuhan University of Science and Technology. With a Ph.D. in Control Science and Control Engineering, his expertise spans image processing, computer vision, pattern recognition, and deep learning. He has contributed to top-tier journals like IEEE Transactions on Image Processing and IEEE Transactions on Circuits and Systems for Video Technology. His research focuses on enhancing image quality, exposure interpolation, and neural augmentation techniques. Previously, he worked as a postdoctoral researcher and an assistant communication engineer, gaining extensive experience in electronic engineering and AI-driven image enhancement. 🚀🔬
Professional Profile
Education & Experience📚💡
📚 Education:
- 🎓 Bachelor’s Degree (2009–2013): Wuhan University of Science and Technology, Electronic Information Engineering
- 🎓 Master’s Degree (2015–2018): Wuhan University of Science and Technology, Electronics and Communication Engineering
- 🎓 Ph.D. (2018–2021): Wuhan University of Science and Technology, Control Science and Control Engineering
💼 Professional Experience:
- 🔧 Assistant Communication Engineer (July 2013 – October 2015) – PLA 6907 Factory
- 🧑🔬 Postdoctoral Researcher (October 2021 – October 2023) – Wuhan University of Science and Technology
- 🎓 Lecturer (October 2023 – Present) – Wuhan University of Science and Technology
Professional Developmen t🚀🔬
📢 zheng chaobing has been actively involved in pioneering deep learning-based image processing technologies. His expertise lies in enhancing low-light images, removing rain streaks, and improving HDR imaging using neural networks. 🌟 He has published multiple high-impact papers in IEEE Transactions and top conferences like ICASSP and CCDC. 💡 His interdisciplinary approach merges traditional signal processing with AI-driven neural augmentation, making significant contributions to computer vision and pattern recognition. 🖥️ As a lecturer, he is passionate about mentoring young researchers, fostering innovation, and driving cutting-edge advancements in image enhancement. 🚀
Research Focus 🔬📖
🔬 zheng chaobing‘s research falls under the Artificial Intelligence and Computer Vision category. His work primarily focuses on image processing 🖼️, pattern recognition 🎭, and deep learning 🤖. He specializes in single image enhancement techniques, including dehazing, deraining, and exposure fusion for low-light and HDR imaging. 🌆 His studies integrate physics-driven models with deep learning algorithms, creating more realistic and high-quality images. 📷 His contributions to AI-powered vision systems are vital for applications in autonomous driving, surveillance, and medical imaging 🏥. His innovations continue to reshape image enhancement methodologies in the AI era. 🚀
Awards & Honors🏅🎖️
🏆 Recognized Publications in Top Journals (IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology)
🏆 Published Over 17 Research Papers in International Conferences & Journals
🏆 Contributor to High-Ranking CCF A, B, and C Category Research Papers
🏆 Recognized for Innovations in AI-driven Image Enhancement Techniques
🏆 Significant Contributions to HDR Imaging, Dehazing, and Deraining Techniques
Publication Top Notes
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Neural Augmentation Based Panoramic High Dynamic Range Stitching
- Authors: Chaobing Zheng, Yilun Xu, Weihai Chen, Shiqian Wu, Zhengguo Li
- Publication: arXiv preprint arXiv:2409.04679
- Year: 2024
- Citations: As of now, this paper has not been cited.
- Access: You can read the full paper on arXiv:
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Single Image De-raining Guided by Fourier Transform Prior
- Authors: Chaobing Zheng, Yao Yao, Wenjian Ying, Shiqian Wu
- Publication: 2023 IEEE International Conference on Image Processing (ICIP)
- Year: 2023
- Citations: This paper currently has no citations.
- Access: The abstract is available on IEEE Xplore:
Conclusion
Zheng Chaobing’s impressive research output, innovation in deep learning-based image processing, and presence in high-impact journals make him a strong candidate for the Best Researcher Award. His contributions significantly advance the field of computer vision and pattern recognition, positioning him as a leader in his domain.