Ph.D. Student at King Mongkut’s University of Technology Thonburi, Thailand
Petcharaporn Yodjai is a dedicated researcher in the field of applied mathematics, with a particular focus on image processing and mathematical modeling. Currently a Ph.D. candidate at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand, she has made significant contributions to the development of advanced techniques in image inpainting and completion. Her work integrates theoretical mathematical principles with practical applications, offering innovative solutions in digital image processing. Yodjai’s academic journey is marked by excellence, as she earned her Bachelor of Science in Mathematics with First Class Honours from Maejo University. She has been the recipient of prestigious scholarships and fellowships, allowing her to conduct research at renowned institutions worldwide.
Profile
Scopus
Education
Yodjai embarked on her academic journey at Maejo University, where she pursued a Bachelor of Science in Mathematics from July 2015 to April 2019. Her outstanding academic performance earned her First Class Honours. Continuing her passion for applied mathematics, she enrolled in the Ph.D. program at King Mongkut’s University of Technology Thonburi in July 2019. Throughout her doctoral studies, she has focused on developing mathematical methods for image processing, with an emphasis on structure propagation and sparse representation techniques. Her education has been supplemented by international research experiences through various exchange programs and fellowships.
Experience
Yodjai has accumulated significant research experience through international collaborations and exchange programs. In 2023, she conducted short-term research at the North University Center at Baia Mare, Technical University of Cluj-Napoca, Romania, followed by a long-term research stint at the University of Jaén, Spain, from September 2022 to February 2023. Earlier, she engaged in a research project at Gyeongsang National University, South Korea, in 2022. Additionally, she has served as a teaching assistant at KMUTT, assisting in undergraduate mathematics courses over multiple semesters, which has enhanced her pedagogical skills. Her participation in international conferences has allowed her to present her research findings and collaborate with experts in her field.
Research Interests
Yodjai’s research interests lie in applied mathematics, specifically in image processing, mathematical modeling, and computational methods. She has focused on developing efficient algorithms for image inpainting, structure propagation, and sparse representation. Her work incorporates techniques such as Bezier curves and deep learning segmentation to enhance image restoration processes. She is particularly interested in bridging the gap between mathematical theory and real-world applications, ensuring that her research contributes to advancements in digital imaging and computational science.
Awards and Scholarships
Yodjai has been recognized for her academic excellence and research contributions through several prestigious scholarships and awards. She is a recipient of the Royal Golden Jubilee Ph.D. Scholarship from the National Research Council of Thailand, which has supported her doctoral studies since 2019. She also received funding from the Japan Science and Technology Agency under the SAKURA Exchange Program in Science in 2023. Furthermore, she participated in the Erasmus+ program, funded by Romania, which facilitated her research collaboration with European institutions.
Publications
Yodjai, P., Kumam, P., & Martínez-Moreno, J. (2025). Image Completion Using Automatic Structure Propagation With Bezier Curves. Mathematical Methods in the Applied Sciences.
Jirakipuwapat, W., Sombut, K., Yodjai, P., & Seangwattana, T. (2025). Enhancing Image Inpainting With Deep Learning Segmentation and Exemplar-Based Inpainting. Mathematical Methods in the Applied Sciences.
Yodjai, P., Kumam, P., Martínez-Moreno, J., & Jirakitpuwapat, W. (2024). Image inpainting via modified exemplar-based inpainting with two-stage structure tensor and image sparse representation. Mathematical Methods in the Applied Sciences, 47(11), 9027-9045.
Awwal, A. M., Wang, L., Kumam, P., Sulaiman, M. I., Salisu, S., Salihu, N., & Yodjai, P. (2023). Generalized RMIL conjugate gradient method under the strong Wolfe line search with application in image processing. Mathematical Methods in the Applied Sciences, 46(16), 17544-17556.
Yodjai, P., Kumam, P., Kitkuan, D., Jirakitpuwapat, W., & Plubtieng, S. (2019). The Halpern approximation of three operators splitting method for convex minimization problems with an application to image inpainting. Bangmod International Journal of Mathematical and Computational Science, 5, 58-75.
Conclusion
Petcharaporn Yodjai’s research contributions in applied mathematics, particularly in image inpainting and completion, demonstrate her dedication to advancing computational methodologies. Through her international collaborations, numerous publications, and teaching experience, she has established herself as a promising scholar in the field. Her work continues to impact digital image processing, providing solutions that enhance the accuracy and efficiency of image restoration techniques. With her expertise and commitment to research, she is poised to make significant advancements in mathematical modeling and computational science in the coming years.