Earticle

MRI 영상에 있어 노이즈를 제거하기 위한 새로운 방법을 제안한다. MRI영상은 실영상과 허상영상으로부터 계산되어진 영상과의 의존적인 Rician 노이즈가 포함되어져 만들어진다. NLM 필터는 추가적인 노이즈에 대해 효과적인 것으로 증명되어진다. 비슷하게도 디노이징 기반 웨이브렛 변환은 더 좋은 노이즈 추정을 한다. 제안된 알고리듬은 기능적 3단계로 디노이징 과정을 수행한다. 첫 번째로 노이즈 영상은 웨이블렛 변환을 적용에 의한 다수의 서브밴드에 나타난다. NLM 필터는 웨이브렛 필터 뱅크를 사용한 신호 분해의 저주파 성분의 서브밴드에 적용되어진다. 실 노이즈 영상에 있어 제거될 노이즈에 매우 효과적으로 판명되어질 다중해상도 NLM 필터는 새로운 디노이징 영상 프레임워크를 만들기 위해 웨이브렛 임계와 결합되어진다. 실험은 알고리듬이 영상 질적 통계에 의해 효과적으로영상 노이즈를 줄일 수 있는 것으로 나타난다.

We propose a new method for the reduction of noise present in the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is corrupted by Rician noise, which is image dependent and computed from both real and imaginary images. Rician noise makes image-based quantitative measurement difficult. The non-local means (NLM) filter has been proven to be effective against additive noise. Similarly, Wavelet transform (WT) based denoising produces a better noise estimation. The proposed algorithm performs denoising in three functional steps. First, the noisy image is decomposed into multiple subbands by using the wavelet transform. NLM filter is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution NLM filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experiments show that the algorithm can reduce image noise effectively in terms of image quality metrics.

본 논문에서는 다중 환경에서의 Wavelet Pulse를 이용한 TR-UWB 시스템의 성능에 대하여 DPSS 펄스와 2차 가우시안 펄스와 비교 분석하였다. TR-UWB 시스템은 데이터 신호와 레퍼런스 신호를 일정 시간 간격을 두고 같이 보낸다. 이때 두 개의 신호는 동일한 채널을 통과 하므로 수신기에서는 이 레퍼런스 신호를 템플릿으로 사용하여 채널 추정 없이 복조 할 수 있어 수신기의 복잡도를 낮출 수 있는 장점이 있다. 하지만 기존의 가우시안 신호 기반의 TR-UWB 시스템은 다중 사용자 환경에서 사용자간의 간섭으로 인한 수신기 성능이 저하되는 단점이 있다 이 단점을 극복하기 위해 DPSS(Discrete Prolate Shperoidal Sequence)를 이용한 사용자간의 간섭을 줄이는 방법이 있다. 본 논문에서 제안하는 기법은 다 해상도 기법을 이용하여 직교 하는 Wavelet 기저 함수를 생성하고 이 함수를 전송 펄스로 이용하여 사용자간의 간섭을 없애 다중 사용자 환경에서 비트 오율 성능이 향상된 시스템을 제안한다.

In this paper, the performance comparison of the TR-UWB System using Wavelet Pulse, DPSS pulse, and second Gaussian pulse in multi-user environment is analyzed. The data signal and the reference signal is sent with some time intervals in TR-UWB system. At this time the two signals are through the same channel. In the receiver the reference signal is used as a template. This advantage results in demodulation without channel estimation and low complexity of it. However the conventional TR-UWB system based on a Gaussian signal in a multi-user environment has the disadvantage of poor performance due to the interferences between the users. To overcome this disadvantage, DPSS (Discrete Prolate Shperoidal Sequence) is used to reduce the interferences between the users. We propose the system with multiresolution function of orthogonal wavelet reducing the interferences, which has a better performance in multi-user environment when they are sent in transmission side.

본 논문은 모바일 ad-hoc 무선 센서 네트워크에서 전력 절약 고속의 멀티미디어 서비스를 지원하는 Cross-Layer 전송 구조(CLTA)를 제안한다. 본 논문의 주요한 목적은 모바일 ad-hoc 무선 센서 네트워크에서 경로들의 생존시간(lifetime)을 증가시키기 위해서 노드들의 이동성 기반위에서 경로의 안정성을 결정하는 방법과 전송전력을 절약하는 방법을 제안하고 보여주는 것이다. 이러한 목적을 달성하기 위해서 본 연구에서는 네트워크 계층 기술과 물리계층 기술의 융합과 상호 유기적인 관계로 인해서 시너지 효과를 얻을 수 있는 Cross-Layer 구조 전략을 제안한다. 기존의 연구들은 주로 고정된 노드들로 구성된 센서 필드 환경에서 연구가 진행된 반면에, 본 논문은 센서 필드에서 고정된 노드들뿐만 아니라 이동 센서노드들도 함께 고려한 좀 더 실제적인 환경에서 연구가 진행된다. 제안된 구조(CLTA)의 성능평가는 시뮬레이션과 이론적인 분석을 통하여 이루어진다.

In this paper, we propose a Cross-Layer Transmission Architecture (CLTA) to support power saving high-speed multimedia services in mobile ad-hoc wireless sensor networks(MAWSN). The main goals of this paper are in showing and proposing how the routing routes are decided on route stability based on mobility of mobile nodes to increase the operational lifetime of routes as well as how the transmit power can be saved in mobile ad-hoc wireless sensor networks. To obtain these goals, we propose a cross-layer architecture strategy which combines network layer technology with physical layer technology to get synergy effects in the view of transmission power saving. We consider a realistic approach, in the points of view of the MAWSN, based on mobile sensor nodes as well as fixed sensor nodes in sensor fields while the conventional research for sensor networks focus on mainly fixed sensor nodes. The performance evaluation of the proposed CLTA is performed via simulation and analysis.

The automatic classification of urban sounds is important for environmental monitoring. In this work we employ SAX-based Multiresolution Motif Discovery to generate features for Urban Sound Classification. Our approach consists in the discovery of relevant frequent motifs in the audio signals and use the frequency of discovered motifs as characterizing attributes. We explore and evaluate different configurations of motif discovery for defining attributes. In the automatic classification step we use a decision tree based algorithm, random forests and SVM. Results obtained are compared with the ones using Mel-Frequency Cepstral Coefficients (MFCC) as features. MFCCs are commonly used in environmental sound analysis, as well as in other sound classification tasks. Experiments were performed on the Urban Sound dataset, which is publicly available. Our results indicate that we can separate difficult pairs of classes (where MFCC fails) using the motif approach for feature construction.

This paper presents an automatic method for detecting transition zones based on multires- olution spectral analysis (MRS). The MRS is calculated over several Fast Fourier Trans- forms (FFT) of different length. It can provide a higher temporal accuracy in the upper spectral region and a better frequency resolution in the lower spectral range. We showcase the importance of this tool by attempting an automatic detection of zones of transition by calculating the Interquartile Range (IQR) of each frame of the MRS FFT. We applied our Visual Assistance of Speech Processing (VASP) System to a corpus. This corpus was in French pronounced by french speakers and has the format CiV CiV with Ci was a stop con- sonant [p t k] and V was a vowel [i e]. The results showed that the automatic detection of transition zones based on MRS provides better results compared to classical spectral analysis of the corpora used.

This paper deals with the problem of multispectral image compression. In particular, we propose to substitute the built-in JPEG 2000 wavelet transform by an adequate multiresolution analysis that we devise within the Lifting-Scheme framework. We compare the proposed method to the classical wavelet transform within both multi-2D and full-3D compression strategies. The two strategies are combined with a PCA decorrelation stage to optimize their performance. For a consistent evaluation, we use a framework gathering four families of metrics including the largely used PSNR. Good results have been obtained showing the appropriateness of the proposed approach especially for images with large dimensions.

Multispectral approach to brain MRI analysis has shown great advance recently in pathology and tissue analysis. However, poor performance of the feature extraction and classification techniques involved in it discourages radiologists to use it in clinical applications. Transform based feature extraction methods like Independent Component Analysis (ICA) and its variants have contributed a lot in this research field. But these global transforms often fails in extraction of local features like small lesions from clinical cases and noisy data. Feature extraction part of the recently introduced Multiresolution Independent Component Analysis (MICA) algorithm in microarray classification is proposed in this work to resolve this issue. Effectiveness of the algorithm in MRI analysis is demonstrated by training and classification with Support Vector Machines (SVM). Both synthetic and real abnormal data from T1-weighted, T2-weighted, proton density, fluid-attenuated inversion recovery and diffusion weighted MRI sequences are considered for detailed evaluation of the method. Tanimoto index, sensitivity, specificity and accuracy of the classified results are measured and analyzed for brain abnormalities, affected white matter and gray matter tissues in all cases including noisy environment. A detailed comparative study of classification using MICA and ICA is also carried out to confirm the positive effect of the proposed method. MICA based SVM is found to yield very good results in anomaly detection, around 2.5 times improvement in classification accuracy is observed for abnormal data analysis.

본 논문은 얼굴 애니메이션에 적합한 다단계 모델 생성 방법을 제안한다. 실시간 얼굴 애니메이션을 위해 기존의 기법으로 생성된 다단계 모델은 표정 생성을 위한 특징들이 소실되어, 낮은 상세 단계의 모델일수록 애니메이션 품질이 떨어진다. 그러나 본 제안방법의 알고리즘은 다단계 얼굴 모델 생성시 애니메이션을 위한 특징 부위가 자동으로 유지되므로, 좋은 품질의 얼굴 애니메이션을 수행 할 수 있다. 제안방법은 생성된 다단계 모델에 대해 얼굴표현 리타겟팅(facial expression retargeting) 기법을 적용시켜 얼굴 애니메이션에 유용함을 보인다.

This paper proposes Multiresolution mesh generation technique that suite for Facial animation. In order to real time facial animation, previous work has shown that featured points are disappeared for generated multiresolution model expression generation. Low level model animations bad quality. But this paper of algorithm automatically preserved feature part when facial model generates for animation, which executes facial animation of good quality. Proposed skill has shown that generated multiresolution model applied to facial expression retargeting technique.

본 논문은 최근 소개된 curvelet 변환 구성을 사용하여 하잇브리드 다초점 이미지 융합 기법을 다룬다. 하잇브리화는 MS 융합 규칙을 새로운 “복제” 방법과 결합시킴으로써 얻어진다. 제안된 기법은 MS 규칙을 사용하여 각 분해 레벨 이미지의 스펙트럼내에 m개의 가장 두드러진 항들만을 융합시킨다. 이 기법은 이미지의 어떠한 스케일과 방향, 이동에서 변환 집합의 MSC에 충실하여 m-항 융합으로 합성이 이루어진다. 제안한 방법을 평가하기 위하여 Xydeas 와 Petrovic이 제안한 경계선에 민감한 객관적 품질 척도를 적용하였다. 실험 결과는 제안한 기법이 잉여, 쉬프트-불변 Dual-Tree 복소수 웨이블릿 변환에 대한 대안으로서의 가능성을 보여주었다. 특히, 50%의 m-항 융합은 어떤 시각적인 품질 저하를 갖지 않는 결과를 주는 것이 확인되었다.

This paper addresses a hybrid multi-focus image fusion scheme using the recent curvelet transform constructions. Hybridization is obtained by combining the MS fusion rule with a novel “copy” method. The proposed scheme use MS rule to fuse the m most significant terms in spectrum of an image at each decomposition level. The scheme is dubbed in this work as m-term fusion in adherence to its use of the MSC (most significant coefficients) in the transform set at any given scale, orientation, and translation. We applied the edge-sensitive objective quality measure proposed by Xydeas and Petrovic to evaluate the method. Experimental results show that the proposed scheme is a potential alternative to the redundant, shift-invariant Dual-Tree Complex Wavelet transforms. In particular, it was confirmed that a 50% m-term fusion produces outputs with no visible quality degradation.

Small PACS based on a personal computer or CR has initially been designed to improve the performance of cost-effective PACS implementation. In that system, Wavelet compression scheme is newly adopted to store images hierarchically to storage unit, and retrieve and display images progressively or display workstation. In this compression method, image is decomposed into subclasses of image by wavelet transform, and then the subclasses of image are vector quantized using a multiresolution codebook.

The ordinary stereo image of an object consists of data of left and right views. Therefore, the left and right image pairs have to be transmitted simultaneously in order to display 3-dimentional video at the remote site. However, due to the twice data in comparing with a monoscopic image of the same object, it needs to be compressed for fast transmission and resource saving. Hence, it needs an effective coding algorithm for compressing stereo image. It was found previously that compressing left and right frames independently will achieve the compression ratio lower than compressing by utilizing the spatial redundancy between both frames. Therefore, in this paper, we study the stereo image compression technique based on the multiresolution wavelet transform using varied disparity-block size for estimation and compensation. The size of disparity-block in the stereo pair subbands are scaling on a coarse-to-fine wavelet coefficients strategy. Finally, the reference left image and residual right image after disparity estimation and compensation are coded by using SPIHT coding. The considered method demonstrates good performance in both PSNR measures and visual quality for stereo image.

We generalize bi-orthogonal (non-orthogona) MRA to frame MRA in which the family of integer translates of a scaling func-tion forms a frame for the initial ladder space V0. We investigate the internal structure of frame MRA and establish the existence of a dual scaling function, and show that, unlike bi-orthogonal MRA, there ex-ists a frame MRA that has no (frame) 'wavelet'. Then we prove the existence of a dual wavelet under the assumption of the existence of a wavelet and present easy sufficient conditions for the existence of a wavelet. Finally we give a new proof of an equivalent condition for the translates of a function in L2(R) to be a frame of its closed linear span.

In this paper, an improved technique for multiresolutive reconstruction of magnetoencephalography (MEG) source distribution is proposed. Using the proposed technique, focal solution with higher energy density can be reconstructed. Moreover, the proposed approach is very easy to implement compared to conventional ones. The usefulness of the proposed technique is verified by the application to a real brain model.

In this paper, a new hierarchical motion estimation (ME) scheme using the wavelet transformed multi-resolution image layers is proposed. While the coarse-to-fine (CtF) ME, used in previously proposed coding schemes, can provide a better estimate at the coarsest resolution, it is difficult to accurately track motion at finer resolution. On the other hand, in fine-to-coarse (FtC) ME, it can solves this local minima problem by estimating motion track at the finest subband and propagating the motion vector (MV) to coarser subband. But this method causes to higher computational overhead. This paper proposes a new method for reducing the computational overhead of fine-to-coarse rnulti-resolution motion estimation (MRME) at the finest resolution level by searching for the region to consider motion vectors of the coarsest resolution subband.

We propose new rotation moment invariants based on multiresolution filter bank techniques. The multiresolution pyramid motivates our simple but efficient feature selection procedure based on the fuzzy C-mean clustering, combined with the Mahalanobis distance. The procedure verifies an impact of random noise as well as an interesting and less known impact of noise due to spatial transformations. The recognition accuracy of the proposed techniques has been tested with the preceding moment invariants as well as with some wavelet based schemes. The numerical experiments, with more than 30,000 images, demonstrate a tangible accuracy increase of about 3% for low noise, 8% for the average noise and 15% for high level noise.

A three-dimensional locally one-dimensional multiresolution time-domain(LOD-MRTD) method is introduced and unconditional stability is proved analytically. The updating formulations have fewer terms on the right-hand side than those of an alternating direction implicit MRTD(ADI-MRTD). The validation of the method is presented using the resonance frequency problem of an empty cavity. The reduction of the numerical dispersion technique is also combined with the proposed method. The numerical examples show that the combined method can improve the accuracy significantly.

This paper presents perceptual model with a stochastic rnultiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the SSQ(successive subband quantization). The watermark embedding is based on the computation of a NVF(noise visibility function) that have local image properties. This method uses non-stationary Gaussian model stationary Generalized Gaussian model because watermark has noise properties. In order to determine the optimal NVF, we consider the watermark as noise. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model use the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark benchmark test.