Jechang Jeong 교수

Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), s

Ultrasound (US) imaging can examine human bodies of various ages; however, in the process of obtaining a US image, speckle noise is generated. The speckle noise inhibits physicians from accurately examining lesions; thus, a speckle noise removal method is essential technology. To enhance speckle noise elimination, we propose a novel algorithm using the characteristics of speckle noise and filtering methods based on speckle reducing anisotropic diffusion (SRAD) filtering, discrete wavelet transfo

Nowadays many camera-based advanced driver assistance systems (ADAS) have been introduced to assist the drivers and ensure their safety under various driving conditions. One of the problems faced by drivers is the faded scene visibility and lower contrast while driving in foggy conditions. In this paper, we present a novel approach to provide a solution to this problem by employing deep neural networks. We assume that the fog in an image can be mathematically modeled by an unknown complex functi

A compression technique for still digital images is proposed with deep neural networks (DNNs) employing rectified linear units (ReLUs). We tend to exploit the DNNs capabilities to find a reasonable estimate of the underlying compression/decompression relationships. We aim for a DNN for image compression purpose that has better generalization property and reduced training time and support real time operation. The use of ReLUs which map more plausibly to biological neurons, makes the training of o

Ultrasound imaging has been used for diagnosing lesions in the human body. In the process of acquiring ultrasound images, speckle noise may occur, affecting image quality and auto-lesion classification. Despite the efforts to resolve this, conventional algorithms exhibit poor speckle noise removal and edge preservation performance. Accordingly, in this study, a novel algorithm is proposed based on speckle reducing anisotropic diffusion (SRAD) and a Bayes threshold in the wavelet domain. In this

A definition of generalized discrete-time time-frequency distributions is introduced. This definition utilizes the full information provided by a data sequence so that one can avoid aliasing which is troublesome in the existing definition. The formulation provides a unified framework for implementing Cohen's class of generalized time-frequency distributions which was formulated in the continuous-time domain. Some requirements for the discrete-time kernel in the approach are discussed in associat

Defines a class of time-frequency representations called variable-windowed spectrograms (VWS), and explores the link between the two independently developed categories of time-frequency representations: Cohen's class and the wavelet transform (WT). By expanding upon the conventional (fixed-windowed) spectrogram, the VWS provides flexible time-frequency localizations depending on the selection of the variable (i.e., time- and frequency-dependent) windows. It is shown that the VWS is a subclass of

We propose a novel blocking artifacts reduction method in image coding based on the least square block discontinuity criterion. We define the "inner product on the block boundaries" for N/spl times/N 2-D functions to introduce the concept of the "boundary-orthogonal 2-D functions". The proposed post-processing approach attempts to find the coefficients of the boundary-orthogonal 2-D functions whose pel-by-pel sum is added to the blocky image to minimize the block discontinuity. Experimental resu

This thesis addresses the issue of high resolution time-frequency representations of signals. Particular attention is paid to the issue of interference reduction while maintaining high resolution. Some new or extended time-frequency representations (TFR) are presented, including the Reduced Interference Distribution (RID), the variable-windowed Short Time Fourier Transform (VWSTFT), and the variable-windowed spectrogram (VWS). The RID and the VWS are members of Cohen's class of generalized time-

We compare two definitions of the instantaneous frequency of a discrete signal: the two-point symmetric phase difference and the discrete-time phase derivative. The phase derivative definition avoids the pitfalls associated with the two-point definition and is equal to the first moment w.r.t. frequency of a properly defined, alias-free time-frequency distribution of the signal, which is consistent with the continuous-time case.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="ht