Prof. Moon Gi Kang

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29Years (1998-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

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29 March 2018 in Sensors

Journal Article

Poisson–Gaussian Noise Analysis and Estimation for Low-Dose X-ray Images in the NSCT Domain

Sangyoon Lee

Min Seok Lee

Moon Gi Kang

The noise distribution of images obtained by X-ray sensors in low-dosage situations can be analyzed using the Poisson and Gaussian mixture model. Multiscale conversion is one of the most popular noise reduction methods used in recent years. Estimation of the noise distribution of each subband in the multiscale domain is the most important factor in performing noise reduction, with non-subsampled contourlet transform (NSCT) representing an effective method for scale and direction decomposition. In this study, we use artificially generated noise to analyze and estimate the Poisson–Gaussian noise of low-dose X-ray images in the NSCT domain. The noise distribution of the subband coefficients is analyzed using the noiseless low-band coefficients and the variance of the noisy subband coefficients. The noise-after-transform also follows a Poisson–Gaussian distribution, and the relationship between the noise parameters of the subband and the full-band image is identified. We then analyze noise of actual images to validate the theoretical analysis. Comparison of the proposed noise estimation method with an existing noise reduction method confirms that the proposed method outperforms traditional methods.

18 May 2016 in Sensors

Journal Article

Color Restoration of RGBN Multispectral Filter Array Sensor Images Based on Spectral Decomposition

Chulhee Park

Moon Gi Kang

A multispectral filter array (MSFA) image sensor with red, green, blue and near-infrared (NIR) filters is useful for various imaging applications with the advantages that it obtains color information and NIR information simultaneously. Because the MSFA image sensor needs to acquire invisible band information, it is necessary to remove the IR cut-offfilter (IRCF). However, without the IRCF, the color of the image is desaturated by the interference of the additional NIR component of each RGB color channel. To overcome color degradation, a signal processing approach is required to restore natural color by removing the unwanted NIR contribution to the RGB color channels while the additional NIR information remains in the N channel. Thus, in this paper, we propose a color restoration method for an imaging system based on the MSFA image sensor with RGBN filters. To remove the unnecessary NIR component in each RGB color channel, spectral estimation and spectral decomposition are performed based on the spectral characteristics of the MSFA sensor. The proposed color restoration method estimates the spectral intensity in NIR band and recovers hue and color saturation by decomposing the visible band component and the NIR band component in each RGB color channel. The experimental results show that the proposed method effectively restores natural color and minimizes angular errors.

1 January 2004 in International Journal of Imaging Systems and Technology

Journal Article

Super‐resolution approach to overcome physical limitations of imaging sensors: An overview

Euncheol Choi

Jongseong Choi

Moon Gi Kang

1 December 2017 in Computer Methods and Programs in Biomedicine

Journal Article

Edge enhancement algorithm for low-dose X-ray fluoroscopic imaging

Min Seok Lee

Chul Hee Park

Moon Gi Kang

13 April 2004 in IEEE Transactions on Image Processing

Journal Article

Spatially Adaptive High-Resolution Image Reconstruction of DCT-Based Compressed Images

Sung Cheol Park

Moon Gi Kang

C.A. Segall

A.K. Katsaggelos

The problem of recovering a high-resolution image from a sequence of low-resolution DCT-based compressed observations is considered in this paper. The introduction of compression complicates the recovery problem. We analyze the DCT quantization noise and propose to model it in the spatial domain as a colored Gaussian process. This allows us to estimate the quantization noise at low bit-rates without explicit knowledge of the original image frame, and we propose a method that simultaneously estimates the quantization noise along with the high-resolution data. We also incorporate a nonstationary image prior model to address blocking and ringing artifacts while still preserving edges. To facilitate the simultaneous estimate, we employ a regularization functional to determine the regularization parameter without any prior knowledge of the reconstruction procedure. The smoothing functional to be minimized is then formulated to have a global minimizer in spite of its nonlinearity by enforcing convergence and convexity requirements. Experiments illustrate the benefit of the proposed method when compared to traditional high-resolution image reconstruction methods. Quantitative and qualitative comparisons are provided.

14 July 2021 in IEEE Access

Journal Article

Poisson-Gaussian Noise Reduction for X-Ray Images Based on Local Linear Minimum Mean Square Error Shrinkage in Nonsubsampled Contourlet Transform Domain

Sangyoon Lee

Moon Gi Kang

Noise reduction is important for X-ray images because it can reduce radiation exposure to patients. X-ray image noise has a Poisson-Gaussian distribution, and recently, noise analysis and removal in multiscale transformations have been widely implemented. The nonsubsampled contourlet transform (NSCT) is a multiscale transformation suitable for medical images that separates the scale and direction. This study proposes a Poisson-Gaussian noise-removal method using NSCT shrinkage that is based on the characteristics of Poisson-Gaussian noise in NSCT domain. It has the structure of a block-matching 3D filtering algorithm in the form of basic estimation and noise removal process; however, the main processes are modified to consider Poisson-Gaussian noise characteristics. In the basic estimation process, an NSCT shrinkage method that is suitable for Poisson-Gaussian noise characteristics is developed by optimizing the local linear minimum mean square error estimator in the NSCT domain. In the denoising step, the noise term of the Wiener filter is determined using the result of the NSCT shrinkage, and finally, the denoised image is obtained. The proposed method is applied to simulated and real X-ray images and is compared with other state-of-the-art Poisson-Gaussian noise removal methods; it exhibits excellent results in both quantitative and qualitative aspects.

About Me

Moon Gi Kang received B.S. and M.S. degrees in electronics engineering from Seoul National University, Seoul, Korea, in 1986 and 1988, respectively, and a Ph.D. degree in electrical engineering from Northwestern University, Evanston, IL, USA, in 1994. He was an Assistant Professor at the University of Minnesota, Duluth, MN, USA, from 1994 to 1997, and since 1997, he has been with the Department of Electronic Engineering, Yonsei University, Seoul, where he is currently a Professor. His current research interests include image and video filtering, restoration, enhancement, and superresolution reconstruction. He has authored more than 100 technical articles in his areas of expertise. He has served as the Editorial Board Member for the IEEE Signal Processing Magazine, the Editor of SPIE Milestone Series Volume (CCD and CMOS imagers), and the Guest Editor of the IEEE Signal Processing Magazine Special Issue on Superresolution Image Reconstruction (May 2003). He has served in the technical program and steering committees of several international conferences. He has also served as the Associate Editor of the EURASIP Journal of Advances in Signal Processing and the Associate Editor of the Digital Signal Processing in Elsevier Journal. He was a recipient of the 2006, 2007, and 2012 Yonsei Outstanding Research Achievement Awards (Technology Transfer), the 2002 HaeDong Foundation Best Paper Award, and the 2000, 2009, and 2010 Awards of Teaching Excellence at Yonsei University.

Prof. Moon Gi Kang

Prof. Moon Gi Kang

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