• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2012년도 춘계학술발표대회논문집
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• pp.6-9
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• 2012

In this study, we compare two image denoising methods which are the Rudin-Osher-Fatemi total variation (TV) model and the nonlocal means (NLM) algorithm on medical images. To evaluate those methods, we used two well known measuring metrics. The methods are tested with a CT image, one X-Ray image, and three MRI images. Experimental result shows that the NML algorithm can give better results than the ROF TV model, but computational complexity is high.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제26권3호
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• pp.156-184
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• 2022

In this article, we propose a novel variational model for restoring color images corrupted by mixed multiplicative Gamma noise and additive Gaussian noise. The model involves a data-fidelity term that characterizes the mixed noise as an infimal convolution of two noise distributions and the saturation-value total variation (SVTV) regularization. The data-fidelity term facilitates suitable separation of the multiplicative Gamma and Gaussian noise components, promoting simultaneous elimination of the mixed noise. Furthermore, the SVTV regularization enables adequate denoising of homogeneous regions, while maintaining edges and details and diminishing the color artifacts induced by noise. To solve the proposed nonconvex model, we exploit an alternating minimization approach, and then the alternating direction method of multipliers is adopted for solving subproblems. This contributes to an efficient iterative algorithm. The experimental results demonstrate the superior performance of the proposed model compared to other existing or related models, with regard to visual inspection and image quality measurements.

• 방송공학회논문지
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• 제20권4호
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• pp.521-532
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• 2015

본 논문은 효과적인 열화영상의 복원을 위해 쿼드트리 구조를 갖는 다중-스케일 블록 지역적 이진 패턴 기반의 영역검출기를 제시하고, 이를 통한 구간적 잡음 제거 기법을 제안한다. 구간적 잡음 제거 기법은 영상 내 전체 화소를 일정한 블록 단위의 영역으로 나누어 화소의 변화량에 따라 검출을 수행하는 다중-스케일 블록 영역 검출기를 쿼드트리 형태로 제시하고 검출된 영역 특성에 맞게 영상분석을 진행한다. 처리되는 영역들은 강한 변화량을 갖는 영역, 약한 변화량을 갖는 영역, 평탄한 영역의 세 가지로 분류되며 차례로 주성분분석, 양방향 필터, 구조-텍스쳐 영상 분해의 기법들이 잡음제거를 위해 적용된다. 객관적 실험결과를 통하여 기존 알고리즘들 보다 제안하는 구간적 잡음 제거 기법이 최대 신호-대-잡음비 측면에서 이득을 가지며, 주관적 화질 비교를 통해 세부정보들이 최대한 보존되어 있음과 동시에 평탄한 영역에 대해서도 왜곡이 거의 없는 향상된 복원영상이 얻어지는 것을 확인할 수 있었다.

• 융합신호처리학회논문지
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• 제4권1호
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• pp.27-35
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• 2003

기존의 웨이블릿 임계치를 이용한 잡음제거 방법은 기저함수가 천이 불변이 되지 않아 불연속점 주위에 의사 깁스 현상을 발생시킨다. 또 논문에서는, 이러한 의사 깁스 현상을 감소시키기 위해 웨이블릿 임치 기법으로 재생성된 웨이블릿 계수의 전변분을 준경도 강화법을 이용하여 최소화하는 방향으로 구현하였다 객관적인 평가는 비실시간상에서 실험하였고 실시간 적용여부는 주위환경의 영향을 고려하여 실시간 신호 획득 보드를 사용하여 확인하였다. 비실 시간의 경우 블록 신호를 예를 들면 기존의 강성 임계치 기법보다 SNR이 2.794dB정도 개선되었고 시각적으로도 불연속점 주위의 의사 깁스 현상이 현격히 감소됨을 확인하였다. 실시간 실험의 경우, 수행시간을 고려하여 반복 횟수를 60번으로 제한한 결과 0.49초의 수행시간이 소요되었고 불연속점 주위의 의사 깁스 현상 역시 제거됨을 확인 할 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제37권4호
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• pp.140-146
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• 2016

The ultrasound imaging in medical diagnosis has become a popular modality because of its safe, noninvasive, portable, relatively inexpensive, and provides a real-time image formation. However, usefulness of ultrasound imaging is at times limited due to the presence of signal-dependent noise like as speckle. Therefore, noise reduction is very important, as various types of noise generated limits the effectiveness of medical image diagnosis. This paper introduces a speckle noise reduce algorithm using total variation denoising (TVD) in Laplacian pyramid. With this method, speckle is removed by TVD of bandpass ultrasound images in Laplacian pyramid domain. For TVD in each pyramid layer, a ${\lambda}$ is selected by trial-and-error method. The visual comparison of despeckled 'in vivo' ultrasound images from pancreas shows that the proposed method could effectively preserve edges and detailed structures while thoroughly suppressing speckle. For a Simulated B-mode image, contrast-to-noise-ratio (CNR) and signal-to-noise-ratio (SNR) were obtained like 4.65 dB and 14.11 dB, respectively. The results show that the proposed method can conduct better than some of the existing methods in terms of the CNR and the SNR.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제6권2호
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• pp.9-24
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• 2002

Total Variation(TV) regularization method is effective for reconstructing "blocky", discontinuous images from contaminated image with noise. But TV is represented by highly nonlinear integro-differential equation that is hard to solve. There have been much effort to obtain stable and fast methods. C. Vogel introduced "the Fixed Point Lagged Diffusivity Iteration", which solves the nonlinear equation by linearizing. In this paper, we apply multigrid(MG) method for cell centered finite difference (CCFD) to solve system arise at each step of this fixed point iteration. In numerical simulation, we test various images varying noises and regularization parameter $\alpha$ and smoothness $\beta$ which appear in TV method. Numerical tests show that the parameter ${\beta}$ does not affect the solution if it is sufficiently small. We compute optimal $\alpha$ that minimizes the error with respect to $L^2$ norm and $H^1$ norm and compare reconstructed images.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1168-1182
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• 2017

In the past decades, various image regularization methods have been introduced. Among them, total variation model has drawn much attention for the reason of its low computational complexity and well-understood mathematical behavior. However, regularization parameter estimation of total variation model is still an open problem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposed in this paper, by means of using the local spectral response, which has the capability of locally selecting the regularization parameters in a content-aware way and therefore adaptively adjusting the weights between the two terms of the total variation model. Experiment results on simulated and real noisy image show the good performance of our proposed method, in visual improvement and peak signal to noise ratio value.

• Communications for Statistical Applications and Methods
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• 제30권2호
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• pp.149-162
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• 2023

The ℓ₁-trend filtering method is one of the most widely used methods for extracting underlying trends from noisy observations. Contrary to the Hodrick-Prescott filtering, the ℓ₁-trend filtering gives piecewise linear trends. One of the advantages of the ℓ₁-trend filtering is that it can be used for identifying change points in piecewise linear trends. However, since the ℓ₁-trend filtering employs total variation as a penalty term, estimated piecewise linear trends tend to be biased. In this study, we demonstrate the biasedness of the ℓ₁-trend filtering in trend level estimation and propose a two-stage bias-reduction procedure. The newly suggested estimator is based on the estimated change points of the ℓ₁-trend filtering. Numerical examples illustrate that the proposed method yields less biased estimates for piecewise linear trends.

• Journal of Magnetics
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• 제21권4호
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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.

• 한국의학물리학회지:의학물리
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• 제29권4호
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• pp.150-156
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• 2018

This study aims to develop an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using anisotropic total variation (ATV) minimization to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The algorithm first applies a filter that integrates the Shepp-Logan filter into a cosine window function on all projections for impulse noise removal. A total variation objective function with anisotropic penalty is then minimized to enhance the difference between the real structure and noise using the steepest gradient descent optimization with adaptive step sizes. The preserving parameter to adjust the separation between the noise-free and noisy areas is determined by calculating the cumulative distribution function of the gradient magnitude of the filtered image obtained by the application of the filtering operation on each projection. With these minimized ATV projections, voxel-driven backprojection is finally performed to generate the reconstructed images. The performance of the proposed algorithm was evaluated with the catphan503 phantom dataset acquired with the use of a low-dose protocol. Qualitative and quantitative analyses showed that the proposed ATV minimization provides enhanced CBCT reconstruction images compared with those generated by the conventional FDK algorithm, with a higher contrast-to-noise ratio (CNR), lower root-mean-square-error, and higher correlation. The proposed algorithm not only leads to a potential imaging dose reduction in repeated CBCT scans via lower mA levels, but also elicits high CNR values by removing noisy corrupted areas and by avoiding the heavy penalization of striking features.