AI and ML researcher

Education

• Doctor of Philosophy in Information Science (Dual degree), Japan Advanced Institute of Science and Technology (JAIST), Japan (SIIT-JAIST-NSTDA Scholarship)
• Doctor of Philosophy in Engineering and Technology (Dual degree), The Sirindhorn International Institute of Technology (SIIT), Thailand (SIIT-JAIST-NSTDA Scholarship)

Publications

	Parametric Regularization Loss in Super Resolution Reconstruction S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, Minh Le Nguyen, C.Haruechaiyasak. [paper]
	Parametric Loss based Super-Resolution for Scene Text Recognition S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, T. Racharak, Minh Le Nguyen, C.Haruechaiyasak , and T. Yamasaki. [paper] / [code]
	Bridging Human Perception and Vision‑Language Models: A Perspective on Explainable Aesthetic Evaluation (Invited Paper) S. Viriyavisuthisakul and T. Yamasaki AIxMM, 2026
	A Web Demo Interface for Explainable Image Aesthetic Evaluation Using Vision-Language Models S. Viriyavisuthisakul, S. Yoshida, P. Sanguansat and T. Yamasaki MIPR, 2025 [VDO] / [code] / [paper]
	Interpretable Aesthetic Assessment and Prompt-Guided Image Retouching S. Viriyavisuthisakul, P. Sanguansat and T. Yamasaki AI-SIPM at MIPR, 2025 [paper]
	Undertrained Image Reconstruction for Realistic Degradation in Blind Image Super-Resolution R. Ito, S. Viriyavisuthisakul, K.Kawamoto and K. Hiroshi arXiv, 2025 [paper]
	A Web Demo Interface for Super-Resolution Reconstruction with Parametric Regularization Loss S. Viriyavisuthisakul, P. Sanguansat and T. Yamasaki ICMR, 2024 [paper] / [code]/ [VDO]
	Optimizing Fixed Window Settings for Classification of Ischemic Stroke in Noncontrast Cranial CT Images S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, T. Yamasaki and D. Songsaeng IFMIA, 2025
	Explainable AI for Image Aesthetic Evaluation Using Vision-Language Models S. Viriyavisuthisakul, P. Sanguansat and T. Yamasaki AIxMM, 2025 [paper]
	A Comprehensive Study of Scene Text Recognition in Scene Text Image Super-Resolution with Parametric Frameworks S. Viriyavisuthisakul, P. Sanguansat and T. Yamasaki ICCE, 2024 [paper]
	The Adaptive Framework for Transformer-based Super Resolution: A Comparative Study for Scene Text Recognition S. Viriyavisuthisakul, P. Sanguansat, and T. Yamasaki WiCV in ECCV 2024
	Comparative Evaluation of Fixed Windowing Strategies on CT Brain Images Using Multiple Deep Learning Models S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, T. Yamasaki and D. Songsaeng SITIS, 2023 [paper]
	Comparative Evaluation of Fixed Windowing Strategies on CT Brain Images Using Multiple Deep Learning Models S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, T. Yamasaki and D. Songsaeng WiML in NeurIPS 2023
	Parametric Regularization Loss in Super-Resolution Reconstruction S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, T. Racharak, Minh Le Nguyen, C.Haruechaiyasak , and T. Yamasaki WiCV in CVPR 2023
	Parametric Loss based Super-Resolution for Scene Text Recognition S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, T. Racharak, Minh Le Nguyen, C.Haruechaiyasak , and T. Yamasaki WiCV in ICCV 2023
	A Regularization-based Generative Adversarial Network for Single Image Super-Resolution S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, T. Racharak, Minh Le Nguyen, C.Haruechaiyasak , and T. Yamasaki IMQA, 2022 [paper]
	Evaluation of Window Parameters of Noncontrast Cranial CT Brain Images for Hyperacute and Acute Ischemic Stroke Classification with Deep Learning S. Viriyavisuthisakul, N. Kaothanthong, P. Sanguansat, C. Haruechaiyasak, Minh Le Nguyen, S. Sarampakhul, T. Chansumpao, D. Songsaeng IEOM, 2022 [paper]
	Water Hyacinth Segmentation for Aquatic Weed Elimination in Thailand S. Viriyavisuthisakul, P. Sanguansat, S. Toriumi, M. Hayashi and T. Yamasaki SITIS, 2018 [paper]
	An Automatic People Counter in Stores Using a Low-Cost IoT Sensing Platform (Best Paper Award) S. Viriyavisuthisakul, P. Sanguansat, S. Toriumi, M. Hayashi and T. Yamasaki IIH-MSP, 2017 [paper]
	Automatic queue monitoring in store using a low-cost IoT sensing platform S. Viriyavisuthisakul, P. Sanguansat, S. Toriumi, M. Hayashi and T. Yamasaki ICCE-TW, 2017 [paper]