• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.6-12
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• 2013

Mammography is the most common technique for the early detection of breast cancer. To diagnose correctly and treat of breast cancer efficiently, many image enhancement methods have been developed. This paper presents a nonlinear image enhancement method for the enhancement of digital mammogram. The proposed method is composed of a nonlinear function for brightness improvement and a nonlinear filter for contrast enhancement. The nonlinear function improves the brightness of dark area and extends the dynamic range of bright area, and the nonlinear filter efficiently enhances the specific regions and objects of the mammogram. The final enhanced image was obtained by combining the processed image with the nonlinear function and the filtered image with the nonlinear filter. The proposed nonlinear image enhancement method was confirmed the enhanced performance comparing with other existing methods.

• The Journal of the Acoustical Society of Korea
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• v.41 no.1
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• pp.30-37
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• 2022

This paper compares and evaluates model performance from two perspectives according to the learning target and network structure for training Deep Neural Network (DNN)-based speech enhancement models in the frequency domain. In this case, spectrum mapping and Time-Frequency (T-F) masking techniques were used as learning targets, and a real network and a complex network were used for the network structure. The performance of the speech enhancement model was evaluated through two objective evaluation metrics: Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) depending on the scale of the dataset. Test results show the appropriate size of the training data differs depending on the type of networks and the type of dataset. In addition, they show that, in some cases, using a real network may be a more realistic solution if the number of total parameters is considered because the real network shows relatively higher performance than the complex network depending on the size of the data and the learning target.

• Journal of Welding and Joining
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• v.33 no.4
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• pp.50-56
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• 2015

In general, discontinuity of metallurgical and structural points of weld zone could decline the fatigue strength. For the lightweight trend, the AHSS application in automotive chassis is in-progress. However, there are few research reports on AHSS welds fatigue strength in especially automotive chassis parts. Therefore, in this study, we evaluated the effects of the factors affecting the AHSS welding fatigue strength. As the result, the stress concentration of weld bead is the most important factor for welding fatigue strength. For the enhancement of welding fatigue strength, we focused on reducing the stress concentration of the welding beads. So, we applied and proved the plasma welding process and GTAW (Gas Tungsten Arc Welding) dressing method. It was verified by uniaxial fatigue specimen, fatigue performance increased from 40 to 60% by applying TIG dressing method compared to the conventional GMAW (Gas Metal Arc Welding). These results could be recommended the enhancement of fatigue performance of AHSS.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.6
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• pp.663-671
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• 1988

In this paper, in order to reduce the blocking effect of block coded images, we propose the method considering the lowpass and bandpass components of Granrath's human visual model. This method consists of two-stage enhancement procedure. The first step is lowpass filtering which smooths out the blocking effect, and the second step is a high frequency enhancement procedure to increase the contrast decreased by the lowpass filtering in the first step. In the first step, the one-dimensional Gaussian filter which aligthns parallel to the edge direction is considered to preserve the edge in the block and the two-dimensional Gaussian filter is used to smooth out the blocking effect near the block boundaries. In the second step, the lowpass and bandpass components of the Granrath's model are considered to increase contrast in a restored image. The performance comparison of the proposed method and the existing mehtods is made by a computer simulation with several block coded images. We can see that the enhancement in the subjective quality of images of the proposed method is more significant than the enhancement in the subjective quality of images of the proposed method is more significant than the existing methods, though the proposed method does not show better performance on the PSNR gain, the poor measure of picture quality for block coded images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1161-1168
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• 2003

In this paper, we propose an improved statistical model-based voice activity detection algorithm and threshold update method. We first improve signal-to-noise ratio by using speech enhancement preprocessing algorithm combined power subtraction method and matched filter, then apply it to LLR test optimum decision rule for improving the performance even in low SNR conditions. And we propose an adaptive threshold update method that was not concerned in any papers. We also perform extensive computer simulations to demonstrate the performance improvement of the proposed VAD algorithm employing the proposed speech enhancement preprocessing algorithm and adaptive threshold update method under various background noise environments. Finally we verify our results by comparing ITU-T G.729 Annex B.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.119-124
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• 2015

Speech enhancement has been required as a preprocessor for a noise robust speech recognition system. Codebook-based Speech Enhancement (CBSE) is highly robust in nonstationary noise environments compared with conventional noise estimation algorithms. However, its performance is severely degraded for the codevector combinations that have lower correlation with the input signal since CBSE depends on the trained codebook information. To overcome this problem, only the reliable codevector combinations are selected to be used to remove the codevector combinations that have lower correlation with input signal. The proposed method produces the improved performance compared to the conventional CBSE in terms of Log-Spectral Distortion (LSD) and Perceptual Evaluation of Speech Quality (PESQ).

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.1-6
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• 2022

Exploration has always been an instinct of humans, and underwater life is as fascinating as it seems. So, for studying flora and fauna below water, there is a need for high-quality images. However, the underwater images tend to be of impaired quality due to various factors, which calls for improved and enhanced underwater images. There are various Histogram Equalization (HE) based techniques which could aid in solving these issues. Classifying the HE methods broadly, there is Global Histogram Equalization (GHE), Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE). Each of these HE extensions have their own pros and cons and thus, by considering them we have considered BBHE, CLAHE, BPDHE, BPDFHE, and DSIHE enhancement algorithms, which are based on Mean Brightness Preserving HE and Local HE, for this study. The performance is evaluated with non-reference performance measures like Entropy, UCIQE, UICM, and UIQM. In this study, we apply the enhancement algorithms on 300 images from the UIEB benchmark dataset and then apply the techniques of cascading fusion on the best-performing algorithms.

• Journal of Medicine and Life Science
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• v.16 no.1
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• pp.1-5
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• 2019

Our purpose was to evaluate usefulness of the contrast-enhanced 3 dimensional fluid attenuated inversion recovery (3D-FLAIR) technique of half brain volume to diagnose the patients with facial neuritis based on segment-based analysis. We assessed retrospectively 17 consecutive patients who underwent brain MR imaging at 3 tesla for facial neuritis: 11 patients with idiopathic facial neuritis and 6 with herpes zoster oticus. Contrast enhanced 3D-FLAIR sequences of the half brain volume were analyzed and 3D T1-weighted sequence of the full brain volume were used as the base-line exam. Enhancement of the facial nerve was determined in each segment of 5 facial nerve segments by two radiologists. Sensitivity, specificity and accuracy of enhancement of each segment were assessed. The authors experienced a prompt fuzzy CSF enhancement in the fundus of the internal auditory canal in patients with enhancement of the canalicular segment. Interobserver agreement of CE 3D-FLAIR was excellent(${\kappa}$-value 0.885). Sensitivity, specificity, and accuracy of each segment are 1.0, 0.823, 0.912 in the canalicular segment; 0.118, 1.0, 0.559 in the labyrinthine segment; 0.823, 0.294, 0.559 in the anterior genu; 0.823, 0.529, 0.676 in the tympanic segment; 0.823, 0.235, 0.529 in the mastoid segment, respectively. In addition, those of prompt fuzzy enhancement were 0.647, 1.0, and 0.824, respectively. Incidence of prompt fuzzy enhancement with enhancement of the canalicular segment was 11 sites(55%): 6 (54.5%) in idiopathic facial neuritis and 5 (83.3%) in herpes zoster. Enhancement of the canalicular segment and prompt fuzzy enhancement on CE 3D-FLAIR was significantly correlated with occurrence of facial neuritis (p<0.001). CE 3D-FLAIR technique of the half brain volume is useful to evaluate the patients with facial neuritis as an adjunct sequence in addition to contrast-enhanced 3D T1-weighted sequence. On segment-based analysis, contrast enhancement of the canalicular segment is the most reliable. Prompt fuzzy enhancement is seen in not only herpes zoster, but in idiopathic facial neuritis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.903-908
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• 2006

As environmental vibration requirements on precision equipment become more stringent. use of pneumatic isolators has become more popular and their performance is subsequently required to be further improved. Dynamic performance of passive pneumatic isolators is related to various design parameters in a complicated manner and, hence, is very limited especially in low frequency range by volume of chambers. In this study, an active control technique, so called time delay control which is considered to be adequate for a low frequency or nonlinear system, is applied to a single chamber pneumatic isolator. The procedure of applying the time delay control law to the pneumatic isolator is presented and its effectiveness in enhancement of transmissibility performance is shown based on simulation and experiment. Comparison between passive and active pneumatic isolators is also presented.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.923-936
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• 2014

Images can be contaminated with different types of noise, for different reasons. The neighborhood averaging and smoothing by image averaging are the classical image processing techniques for noise removal. The classical spatial filtering refers to the aggregate of pixels composing an image and operating directly on these pixels. To reduce or remove effectively noise in image sequences, it usually needs to use noise reduction filter based on space or time domain such as method of spatial or temporal filter. However, the method of spatial filter can generally cause that signals of objects as the target are also blurred. In this paper, we propose temporal filter using the piece-wise quadratic function model and enhancement algorithm of image quality for the performance improvement of target detection and tracking by heterogeneous noise reduction. Image tracking simulation that utilizes real IIR(Imaging Infra-Red) images is employed to evaluate the performance of the proposed image processing algorithm.