Seoul National University · Computer Science
Professor Myungjoo Kang's research lab specializes in image processing and computer vision, with a focus on developing advanced variational and optimization methods for image restoration under non-Gaussian noise models. The lab investigates speckle reduction in coherent imaging systems—particularly in synthetic aperture radar—by leveraging total variation-based models and convex optimization techniques. A key research direction involves designing efficient and stable algorithms through mathematical transformations, such as logarithmic and root-based mappings, to improve the convexity and convergence of variational models. The lab also contributes to the theoretical foundations of maximum a posteriori estimation in inverse problems with multiplicative noise.
Figures are computed from collected data and may differ slightly.
The fully developed speckle (multiplicative noise) naturally appears in coherent imaging systems, such as synthetic aperture radar. Since the speckle is multiplicative, it is difficult to interpret observed data. Total variation (TV) based variational models have recently been used in the removal of the speckle because of the strong edge preserving property of TV and reasonable computational cost. However, the fidelity term (or negative log-likelihood) of the original variational model [G. Auber
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