• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.4
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• pp.365-375
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• 2014

This paper proposes an abnormal V-shaped-error-free non-blind deconvolution technique featuring an adaptively segmented forward-problem based iterative deconvolution (ASDCN) process. Unlike the algebraic based inverse operations, this eliminates any operations of differential and division by zero to successfully circumvent the issue on the abnormal V-shaped error. This effectiveness has been demonstrated for the first time with applying to a real analysis for the effects of the Random Telegraph Noise (RTN) and/or Random Dopant Fluctuation (RDF) on the overall SRAM margin variations. It has been shown that the proposed ASDCN technique can reduce its relative errors of RTN deconvolution by $10^{13}$ to $10^{15}$ fold, which are good enough for avoiding the abnormal ringing errors in the RTN deconvolution process. This enables to suppress the cdf error of the convolution of the RTN with the RDF (i.e., fail-bit-count error) to $1/10^{10}$ error for the conventional algorithm.

• 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.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.6
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• pp.609-617
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• 2020

Non-destructive inspection using X-rays is used as a method to check the inside of products. In order to accurately inspect, a X-ray image requires a higher spatial resolution. However, the reduction in pixel size of the X-ray detector, which determines the spatial resolution, is time-consuming and expensive. In this regard, a DMM has been proposed to obtain an improved spatial resolution using the same X-ray detector. However, this has a limitation that the motion blur phenomenon, which is a decrease in spatial resolution. In this paper, motion blur was removed by applying Non-Blind Deconvolution to the DMM image, and the increase in spatial resolution was confirmed. DMM and Non-Blind Deconvolution were sequentially applied to X-ray images, confirming 62 % MTF value by an additional 29 % over 33 % of DMM only. In addition, SSIM and PSNR were compared to confirm the similarity to the 1/2 pixel detector image through 0.68 and 33.21 dB, respectively.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4591-4596
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• 2023

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.277-286
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• 2022

Due to the uniquely slow convergence speed of the EM algorithm, it suffers form a lot of processing time until the desired deconvolution image is obtained when the image is large. To cope with the problem, in this paper, an immediate solution of the EM algorithm is provided under the Gaussian image model. It is derived by finding the recurrent formular of the EM algorithm and then substituting the results repeatedly. In this paper, two types of immediate soultion of image deconboution by EM algorithm are provided, and both methods have been shown to work well. It is expected that it free the processing time of image deconvolution because it no longer requires an iterative process. Based on this, we can find the statistical properties of the restored image at specific iterates. We demonstrate the effectiveness of the proposed method through a simple experiment, and discuss future concerns.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.114-117
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• 2015

본 논문에서는 Non-blind 디컨벌루션 알고리듬 중 하나인 Richardson-Lucy(RL) 디컨벌루션을 영상 분할을 통해 성능을 향상시킨 알고리듬을 제안한다. RL 디컨벌루션은 영상의 크기가 커질수록 연산 양이 크게 증가한다. 따라서 크기가 큰 영상의 RL 디컨벌루션은 계산에 많은 시간을 필요로 한다. 이를 개선하기 위하여 영상을 적절한 크기로 분할하여 각각 RL 디컨벌루션을 계산한다. 또한 분할 시 생기는 왜곡을 줄이기 위해 리플 제거를 위한 알고리듬을 추가한다. 이를 통해 기존의 알고리듬보다 연산 양을 줄여 빠른 RL 디컨벌루션이 가능하도록 개선한다.