• 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.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.619-628
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• 2012

Noise reduction is an important issue in the field of image processing because image noise lowers the quality of the original pure image. The basic difficulty is that the noise and the signal are not easily distinguished. Simple smoothing is the most basic and important procedure to effectively remove the noise; however, the weakness is that the feature area is simultaneously blurred. In this research, we use ways to measure the degree of noise with respect to the degree of image features and propose a Bayesian noise reduction method based on MAP (maximum a posteriori). Simulation results show that the proposed adaptive noise reduction algorithm using Bayesian MAP provides good performance regardless of the level of noise variance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.909-914
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• 2002

In this thesis, noise reduction of image with impulse noise in circle image removed noise to harness existing median filter for noise reduction from image data of damage by noise when impulse noise is high or noise reduction is low, but it is not made up of noise reduction to harness existing median filter in case of existence of non-impulse noise. Therefore noise reduction of image with non-impulse noise had to remove noise by morphological arithmetic in this thesis's proposition. In contrast to median filtering, result of edge detection is more efficient after remove non-impulse noise by method of thesis's proposition and it compare and demonstrate through this experimentation.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.225-235
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• 2012

Noise reduction is an important issue in the field of image processing because image noise worsens the quality of the input image. The basic difficulty is that the noise and the signal are not easy to distinguish. Simple moothing is one of the most basic and important procedures to remove the noise, however, it does not consider the level of noise. This method effectively reduces the noise but the feature area is simultaneously blurred. This paper considers the block approach to detect noise and image features of the input image so that noise reduction could be adaptively applied. Simulation results show that the proposed algorithm improves the overall quality of the image by removing the noise according to the noise level.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.623-628
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• 2019

The aim of this study was to design fast non local means (FNLM) noise reduction algorithm and to confirm its application feasibility in light microscopic image. For that aim, we acquired mouse first molar image and compared between previous widely used noise reduction algorithm and our proposed FNLM algorithm in acquired light microscopic image. Contrast to noise ratio, coefficient of variation, and no reference-based evaluation parameter such as natural image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) were used in this study. According to the result, our proposed FNLM noise reduction algorithm can achieve excellent result in all evaluation parameters. In particular, it was confirmed that the NIQE and BRISQUE evaluation parameters for analyzing the overall morphologcal image of the tooth were 1.14 and 1.12 times better than the original image, respectively. In conclusion, we demonstrated the usefulness and feasibility of FNLM noise reduction algorithm in light microscopic image of small animal tooth.

• 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.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.905-914
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• 2011

In the field of image processing, the removal noise contamination from the original image is essential. However, due to various reasons, the occurrence of the noise is practically impossible to prevent completely. Thus, the reduction of the noise contained in images remains important. In this study, we estimate the level of noise variance based on the measurement of the relative strength of the noise, and we propose a noise reduction algorithm that uses a sigma filter. As a result, the proposed statistical noise reduction methodology provides significantly improved results over the usual sigma filtering regardless of the level of the noise variance.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1462-1467
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• 2013

A pseudo 480-Hz drive method has been proposed to reduce the dynamic false contour noise that occurs on flat panel displays with displaying grayscale image in the digital mode, such as plasma display panels. The proposed method makes the image movements nearly continuous by rearranging the 8-bit image data displayed for 1 TV field into 8 subfields. The position of the image data rearranged in subfields has been optimized on the basis of the speed of the moving image by computer simulations for the dynamic false contour noise. It is verified that a significant reduction in the dynamic false contour noise is achieved with the proposed method as compared to the conventional noise reduction technologies. Moreover, to reduce the noise in digital mode displays, the proposed technology requires only 8 subfields. Therefore, there is no reduction in the brightness of the image.

• Journal of radiological science and technology
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• v.44 no.3
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• pp.225-229
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• 2021

The digital image such as medical X-ray and nuclear medicine field mainly contains noise distribution. The noise degree in image degrades image quality. That is why, the noise reduction algorithm is efficient for medical image field. In this study, we confirmed effectiveness of application for median modified Wiener filter (MMWF) algorithm for noise reduction in PET/MR image compared with median filter image, which is used as conventional noise redcution algorithm. The Jaszczak PET phantom was used by using 18F solution and filled with NaCl+NiSO4 fluids. In addition, the radioactivity ratio between background and six spheres in the phantom is maintained to 1:8. In order to mimic noise distribution in the image, we applied Gaussian noise using MATLAB software. To evlauate image quality, the contrast to noise ratio (CNR) and coefficient of variation (COV) were used. According to the results, compared with noise image and images with MMWF algorithm, the image with MMWF algorithm is increased approximately 33.2% for CNR result, decreased approximately 79.3% for COV result. In conclusion, we proved usefulness of MMWF algorithm in the PET/MR images.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.555-572
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• 2018

Noise reduction processes that reduce or eliminate noise (caused by a variety of reasons) in noise contaminated image is an important theme in image processing fields. Many studies are being conducted on noise removal processes due to the importance of distinguishing between noise added to a pure image and the unique characteristics of original images. Adaptive filter and sigma filter are typical noise reduction filters used to reduce or eliminate noise; however, their effectiveness is affected by accurate noise estimation. This study generates a distribution of noise contaminating image based on a Dirichlet normal mixture model and presents a Bayesian approach to distinguish the characteristics of an image against the noise. In particular, to distinguish the distribution of noise from the distribution of characteristics, we suggest algorithms to develop a Bayesian inference and remove noise included in an image.