• The Journal of the Acoustical Society of Korea
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• v.27 no.1E
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• pp.1-10
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• 2008

A noise suppression algorithm is proposed for nonstationary noisy environments. The proposed algorithm is different from the conventional approaches such as the spectral subtraction algorithm and the minimum statistics noise estimation algorithm in that it classifies speech and noise signals in time-frequency bins. It calculates the ratio of the variance of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. If the ratio is greater than an adaptive threshold, speech is considered to be present. Our adaptive algorithm tracks the threshold and controls the trade-off between residual noise and distortion. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of simplicity and light computational load for estimating the noise. This algorithm reduces the residual noise significantly, and is superior to the conventional methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.63-69
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• 2017

In order to observe the noise property of the flat-panel digital radiography detector, measuring the normalized noise power spectrum (NNPS) from acquired x-ray images is conducted. However, the conventional NNPS measurement has an unstable property depending on the acquired image. Averaging the sample periodograms of the input image is usually performed to estimate the NNPS values and increasing the number of samples can provide a reliable NNPS measurement. In this paper, for a finite number of images, two measurement methods, which are based on averaging spectra, such as the image periodogram, are proposed and their performances are analyzed. Using x-ray images acquired from two types of radiography detectors, the two spectrum averaging methods are compared and it is shown that averaging spectra based on the maximal number of combinations of the image pairs provides the best performance in measuring NNPS.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.120-127
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• 2016

In order to acquire digital x-ray images, developing radiography detectors have been recently conducted based on the DR (digital radiography) technology. The noise property of the radiography detector can be observed from measuring the NNPS (normalized noise power spectrum) using uniform exposure images. Here, the image difference of two images is used to remove the fixed pattern noise in measuring the detector NNPS. In this paper, two average images are first calculated using several images and then their difference is used to calculate an NNPS value. Here, the obtained NNPS value is usually lower than the true detector NNPS due to the average. Hence, a compensation constant, which is a function of the number of used images, is also proposed to compensate the NNPS value to obtain the true detector NNPS. Furthermore, another measurement method, in which the ratio of the average images is used, is proposed. Through NNPS measuring experiments using real x-ray images, it is observed that the proposed method can provide further accurate NNPS measurements.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.818-827
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• 2008

In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.87-94
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• 2009

Noise estimation and suppression are a crucial factor of many speech communication and recognition systems. In this paper, proposed algorithm is based on the ratio of variance normalized of noisy power spectrum in time-frequency domain. Our proposed algorithm tracks the threshold and controls the trade-off between residual noise and distortion. This algorithm is evaluated by the ITU-T P.835 signal distortion (SIG) and segment signal to noise ratio (SNR), and is superior to the conventional methods.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.87-96
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• 2007

In this paper we propose a new noise reduction algorithm for stationary and nonstationary noisy environments. Our algorithm classifies the speech and noise signal contributions in time-frequency bins, and is not based on a spectral algorithm or a minimum statistics approach. It relies on calculating the ratio of the standard deviation of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. We show that good quality can be achieved for enhancement speech signal by choosing appropriate values for ${\delta}_t\;and\;{\delta}_f$. The proposed method greatly reduces the noise while providing enhanced speech with lower residual noise and somewhat higher mean opinion score (MOS), background intrusiveness (BAK) and signal distortion (SIG) scores than conventional methods.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2594-2600
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• 2020

A gamma camera system using radionuclide has a functional imaging technique and is frequently used in the field of nuclear medicine. In the gamma camera, it is extremely important to improve the image quality to ensure accurate detection of diseases. In this study, we designed a blind-deconvolution framework after a noise-reduction algorithm based on a non-local mean, which has been shown to outperform conventional methodologies with regard to the gamma camera system. For this purpose, we performed a simulation using the Monte Carlo method and conducted an experiment. The image performance was evaluated by visual assessment and according to the intensity profile, and a quantitative evaluation using a normalized noise-power spectrum was performed on the acquired image and the blind-deconvolution image after noise reduction. The result indicates an improvement in image performance for gamma camera images when our proposed algorithm is used.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.719-722
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• 2019

Gamma-ray images generally suffer from a lot of noise because of low photon detection in the gamma camera system. The purpose of this study is to improve the image quality in gamma-ray images using a gamma camera system with a fast nonlocal means (FNLM) noise reduction algorithm with an acceleration function. The designed FNLM algorithm is based on local region considerations, including the Euclidean distance in the gamma-ray image and use of the encoded information. To evaluate the noise characteristics, the normalized noise power spectrum (NNPS), contrast-to-noise ratio (CNR), and coefficient of variation (COV) were used. According to the NNPS result, the lowest values can be obtained using the FNLM noise reduction algorithm. In addition, when the conventional methods and the FNLM noise reduction algorithm were compared, the average CNR and COV using the proposed algorithm were approximately 2.23 and 7.95 times better than those of the noisy image, respectively. In particular, the image-processing time of the FNLM noise reduction algorithm can achieve the fastest time compared with conventional noise reduction methods. The results of the image qualities related to noise characteristics demonstrated the superiority of the proposed FNLM noise reduction algorithm in a gamma camera system.

• Journal of Magnetics
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• v.21 no.4
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• pp.593-598
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• 2016

In order to reduce the amount of noise component in X-ray imaging system, various reduction techniques were frequently used in the field of diagnostic imaging. Although the previous techniques -such as median, Wiener filters and Anscombe noise reduction technique - were able to reduce the noise, the edge information was still damaged. In order to cope with this problem, total variation (TV) noise reduction technique has been developed and researched. The purpose of this study was to evaluate and compare the image quality using normalized noise power spectrum (NNPS) and contrast-to-noise ratio (CNR) through simulations and experiments with respect to the above-mentioned noise reduction techniques. As a result, not only lowest NNPS value but also highest CNR values were acquired using a TV noise reduction technique. In conclusion, the results demonstrated that TV noise reduction technique is proved as the most practical method to ensure accurate denoising in X-ray imaging system.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.439-443
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• 2019

To improve the noise characteristics, software-based noise reduction algorithms are widely used in cadmium zinc telluride (CZT) pixelated semiconductor gamma camera system. The purpose of this study was to develop an improved median filtering algorithm using a thresholding method for noise reduction in a CZT pixelated semiconductor gamma camera system. The gamma camera system simulated is a CZT pixelated semiconductor detector with a pixel-matched parallel-hole collimator and the spatial resolution phatnom was designed with the Geant4 Application for Tomography Emission (GATE). In addition, a noise reduction algorithm with a median filter using an improved thresholding method is developed and we applied our proposed algorithm to an acquired spatial resolution phantom image. According to the results, the proposed median filter improved the noise characteristics compared to a conventional median filter. In particular, the average for normalized noise power spectrum, contrast to noise ratio, and coefficient of variation results using the proposed median filter were 10, 1.11, and 1.19 times better than results using conventional median filter, respectively. In conclusion, our results show that the proposed median filter using improved the thresholding method results in high imaging performance when applied in a CZT semiconductor gamma camera system.