Yanping Mo | Image Restoration Algorithms | Best Researcher Award

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Ms. Yanping Mo | Image Restoration Algorithms | Best Researcher Award

Postgraduate Student | Xi’an University of Science and Technology | China

Ms. Yanping Mo is a researcher affiliated with the School of Communication and Information Engineering at Xi’an University of Science and Technology, China, and associated with the China Education and Research Network in Beijing. Her research primarily focuses on the intersection of computational imaging, signal processing, and optimization algorithms for image restoration and enhancement. In particular, her recent work titled “Research on plug-and-play image restoration algorithm based on dual weighted ADMM,” published in Optics & Laser Technology (December 2025), demonstrates her expertise in developing advanced optimization frameworks for image reconstruction. The study explores a dual weighted Alternating Direction Method of Multipliers (ADMM) approach that integrates plug-and-play priors to enhance the flexibility and accuracy of image restoration tasks. This approach effectively addresses common challenges in image denoising, deblurring, and super-resolution by adaptively balancing data fidelity and regularization terms. Her contribution lies in improving the convergence stability and computational efficiency of traditional ADMM-based algorithms while maintaining high-quality visual outputs. Through her collaborative work with researchers such as Wei Chen, Zhaohui Li, and Bin Fan, Mo advances the application of mathematical modeling and artificial intelligence techniques in optical and laser imaging technologies. Her research supports the broader goal of enhancing image processing methodologies for scientific imaging, remote sensing, medical imaging, and industrial inspection applications. Overall, Yanping Mo’s research reflects a strong commitment to the development of robust and intelligent algorithms that bridge theory and application in the field of computational optics and image restoration.

Profile: ORCID

Featured Publications

1. Chen, W., Mo, Y., Li, Z., & Fan, B. (2025, December). Research on plug-and-play image restoration algorithm based on dual weighted ADMM. Optics & Laser Technology, 113997.

Klara Reichard | Computer Vision Systems | Best Researcher Award

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Mrs. Klara Reichard | Computer Vision Systems | Best Researcher Award

Klara Reichard | Technical University of Munich | Germany

Mrs. Klara Reichard is a PhD candidate at the Technical University of Munich (TUM) and a member of the BMW Doctoral Program, specializing in computer vision, autonomous driving, and vision-language integration. She holds advanced degrees in computation and information sciences and works at the intersection of academia and industry to bridge theoretical research with real-world applications. Her professional experience includes collaborations with BMW Group and the University of Padova, where she has contributed to projects on automatic parking space detection, vocabulary-free semantic segmentation, and language-guided anomaly detection for open-world perception. Klara’s research focuses on developing robust perception systems that enhance the safety and intelligence of next-generation autonomous vehicles, with significant contributions such as novel methods for open-vocabulary and vocabulary-free semantic segmentation and integration into autonomous driving systems. She has authored multiple publications, including contributions to the Journal of Experimental Algorithmics and arXiv preprints, with her work accumulating over 24 citations. Klara holds one patent in progress for open-world segmentation and actively contributes to interdisciplinary research communities. She has been recognized for her innovative approach to bridging cutting-edge computer vision research with deployable industry solutions, demonstrating leadership in advancing intelligent, safe, and scalable autonomous vehicle technologies. Quotes: 25, h-index: 2, i10-index: 2

Profile: Google Scholar

Featured Publications

1. Radermacher M., Reichard K.*, Rutter I., Wagner D., A geometric heuristic for rectilinear crossing minimization. Proc. 20th Workshop on Algorithm Engineering and Experiments, 2018, 12.

2. Radermacher M., Reichard K.*, Rutter I., Wagner D., Geometric heuristics for rectilinear crossing minimization. J. Exp. Algorithmics, 2019, 24, 1–21.

3. Reichard K.*, Rizzoli G., Gasperini S., Hoyer L., Zanuttigh P., Navab N., From open-vocabulary to vocabulary-free semantic segmentation. arXiv preprint arXiv:2502.11891, 2025, 1.

4. Postels J., Strümpler Y., Reichard K.*, Van Gool L., Tombari F., 3D compression using neural fields. arXiv preprint arXiv:2311.13009, 2023, 1.

5. Reichard K.*, Rectilinear Crossing Minimization. Informatics Institute, 2016.