• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.779-781
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• 2013

As the demand for various multimedia service increases the technology that utilizes image as information transfer method develops rapidly. Though average filter, median filter and weight filter etc. have been proposed to remove various noises that are added to images, the existing methods are short of noise removal and edge reservation performance. Therefore, in this paper an algorithm, in which noise is decided at the first hand, and then it is processed through modified median filter and adaptive weighted average filter, is proposed to effectively remove the complex noise that has been added to an image. And it was compared with existing methods through simulation and PSNR(peak signal to noise ratio) has been used as a criterion.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.151-156
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• 2010

This paper is a study on image noise reduction of modified sigma filter by image decomposition using directivity. Conventional sigma filter has been shown to be a good solution both in terms of filtering accuracy and computational complexity. However, the sigma filter does not preserve well small edges especially for high level of additive noise. In this paper, we propose here a new method using a modified sigma filter. In our proposed method the input image is first decomposed in two components that have features of horizontal, vertical and diagonal direction. Then, two components are applied HPF and LPF. By applying a conventional sigma filter separately on each of them, the output image is reconstructed from the filtered components. Added noise is removed and our proposed method preserves the edges from the image. Comparative results from experiments show that the proposed algorithm achieves higher gains, on average, 2.6 dB PSNR than the sigma filter and 0.5 dB PSNR than the modified sigma filter. When relatively high levels of noise added, the proposed algorithm shows better performance than two conventional filters.

• Investigative Magnetic Resonance Imaging
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• v.20 no.4
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• pp.215-223
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• 2016

Purpose: The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called "Slice Encoding for Metal Artifact Correction (SEMAC)" is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts. Materials and Methods: Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, $l_1$ minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions. Results: Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction. Conclusion: We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.877-879
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• 2013

Imaging device such as digital TV is being popular in a modern society based on communication technology. However, because of internal and external cause of system in the process of transmission, storage and acquisition, image is degraded by noise. Therefore, the importance of denoising technology is being increased, and a research for that is being actively made. In this paper, a weighted filter algorithm that considers different pixels of masks and estimated noise variance was proposed. in order to remove AWGN. And, PSNR(peak signal to noise ratio) was used to represent the excellence of proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.76-84
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• 2007

The goal of this paper is to improve the watermarking performance using the following two methods; watermark estimation and low density parity check (LDPC) codes. For a blind watermark decoding, the power of a host image, which is hundreds times greater than the watermark power, is the main noise source. Therefore, a technique that can reduce the effect of the power of the host image to the detector is required. To this end, we need to estimate watermark from the watermarked image. In this paper, the watermark estimation is done by an adaptive estimation method with the generalized Gaussian distribution modeling of sub-band coefficients in the wavelet domain. Since the watermark capacity as well as the error rate can be improved by adopting optimum decoding principles and error correcting codes (ECC), we employ the LDPC codes for the decoding of the estimated watermark. Also, in LDPC codes, the knowledge about the noise power can improve the error correction capability. Simulation results demonstrate the superior performance of the proposed algorithm comparing to LDPC decoding with other estimation-based watermarking algorithms.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.1
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• pp.57-62
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• 2009

Purpose: Brain SPECT study is more sensitive to motion than other studies. Especially, when applying 1-day subtraction method for Diamox SPECT, it needs shorter study time in order to prevent reexamination. We were required to have new study condition and analysing method on dual detector system because triple head camera in Seoul National University Hospital is to be disposed. So we have tried to increase image quality and make the dual and triple head to have equivalent study time by using a new analysing program. Materials and Methods: Using IEC phantom, we estimated contrast, SNR and FWHM. In Hoffman 3D brain phantom which is similar with real brain, we were on the supposition that 5% of injected doses were distributed in brain tissue. To compare with existing FBP method, we used fan-beam collimator. And we applied 15 sec, 25 sec/frame for each SEPCT studies using LEHR and LEUHR. We used OSEM2D and Onco-flash3D reconstruction method and compared reconstruction methods between applied Gaussian post-filtering 5mm and not applied as well. Attenuation correction was applied by manual method. And we did Brain SPECT to patient injected 15 mCi of $^{99m}Tc$-HMPAO according to results of Phantom study. Lastly, technologist, MD, PhD estimated the results. Results: The study shows that reconstruction method by Flash3D is better than exiting FBP and OSEM2D when studied using IEC phantom. Flowing by estimation, when using Flash3D, both of 15 sec and 25 sec are needed postfiltering 5 mm. And 8 times are proper for subset 8 iteration in Flash3D. OSEM2D needs post-filtering. And it is proper that subset 4, iteration 8 times for 15sec and subset 8, iteration 12 times for 25sec. The study regarding to injected doses for a patient and study time, combination of input parameter-15 sec/frame, LEHR collimator, analysing program-Flash3D, subset 8, iteration 8times and Gaussian post-filtering 5mm is the most appropriate. On the other hands, it was not appropriate to apply LEUHR collimator to 1-day subtraction method of Diamox study because of lower sensitivity. Conclusions: We could prove that there was also an advantage of short study time effectiveness in Dual camera same as Triple gamma camera and get great result of alternation from existing fan-beam collimator to parallel collimator. In addition, resolution and contrast of new method was better than FBP method. And it could improve sensitivity and accuracy of image because lesser subjectivity was input than Metz filter of FBP. We expect better image quality and shorter study time of Brain SPECT on Dual detector system.