Yoon Mo Jung
Sungkyunkwan University · 情報科学
研究室紹介
Professor Yoon Mo Jung's research lab specializes in mathematical modeling, optimization, and numerical analysis for inverse problems and image reconstruction, with a strong focus on developing efficient and robust algorithms for real-time applications. The lab's main research directions include total variation-based regularization, non-convex optimization, and advanced image segmentation techniques, particularly in medical imaging and electrical impedance tomography (EIT). The group also explores dynamic systems and control, especially in the context of multi-agent systems with Markov jump parameters and adaptive event-triggered mechanisms. Their work bridges applied mathematics, computational science, and engineering applications, emphasizing theoretical rigor and practical efficiency.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15We propose a novel multiphase segmentation model built upon the celebrated phase transition model of Modica and Mortola in material sciences and a properly synchronized fitting term that complements it. The proposed sine-sinc model outputs a single multiphase distribution from which each individual segment or phase can be easily extracted. Theoretical analysis is developed for the $\Gamma$-convergence behavior of the proposed model and the existence of its minimizers. Since the model is not quad
EIT problem is a typical inverse problem with serious ill-posedness. In general, regularization techniques are necessary for such ill-posed inverse problems. To overcome ill-posedness, the total variation (TV) regularization is widely used and it is also successfully applied to EIT. For realtime monitoring, a fast and robust image reconstruction algorithm is required. By exploiting recent advances in optimization, we propose a first-order TV algorithm for EIT, which simply consists of matrix-vec
We propose a non-convex type total variation model for impulse noise removal by incorporating TV and the quasi-norm $\ell_q $, $0 < q < 1 $. Since the proposed model is non-convex and non-smooth, an iteratively reweighted algorithm is adapted and combined with a linearized ADMM. The convergence of the proposed algorithm is established and numerical results are given to illustrate the validity and efficiency of the proposed model.
Abstract Trend filtering aims to estimate underlying trends in time series data, which is necessary to investigate data in a variety of disciplines. We propose a new method called elastic trend filtering . The proposed method combines ℓ 2 and ℓ 1 norm penalties to exploit the benefits and strengths of Hodrick–Prescott and ℓ 1 trend filterings. We apply the alternating direction method of multipliers for its efficient computation and numerical experiments show the soundness and efficiency of the