Noise cross PSD estimation using phase information in diffuse noise field

Among various speech enhancement methods, two-microphone noise reduction systems are utilized for their low cost implementation and acceptable performance. Coherence-based methods are well known as efficient two-microphone noise reduction techniques. These techniques, however, do not work well when the received noise signals are correlated. Coherence-based methods can be improved when the cross power spectral density (CPSD) of input noises is available. In this paper, we propose a new method for estimating noise CPSD based on the assumption of a diffuse noise field. With this assumption, we estimate the noise CPSD using phase information. Then, the estimated noise CPSD is used to calculate a coherence-based gain filter which is then employed to enhance noisy signals. We compare the proposed phase-based noise CPSD estimation with a noise CPSD estimation technique based on a voice activity detector (VAD), both of which are herein separately employed in a two-microphone speech enhancement configuration. The comparison shows that the two-microphone speech enhancement scheme utilizing the proposed noise CPSD estimation technique outperforms the enhancement system using the VAD-based noise CPSD estimation.     

Design of 3-D Noncausal Filters with Small Roundoff Noise and No Overflow Oscillations

The contribution of this paper consists of two individual parts. First, an invertible mapping technique is presented for 3-D digital system design, and it is applied to approximate 3-D noncausal filters in the spatial domain. Secondly, an algorithm is proposed for obtaining a structure for 3-D IIR filters with small roundoff noise and no overflow oscillations. The design of noncausal filters can be carried out by three steps: 1), a given noncausal impulse response is transformed into the first octant using the proposed 3-D invertible mapping technique; 2), the transformed impulse response in the first octant is approximated by balanced model reduction of 3-D separable denominator systems;3), the resultant 3-D IIR filter is transformed back to the original coordinates.     

Robust white matter lesion segmentation in FLAIR MRI

This paper discusses a white matter lesion (WML) segmentation scheme for fluid attenuation inversion recovery (FLAIR) MRI. The method computes the volume of lesions with subvoxel precision by accounting for the partial volume averaging (PVA) artifact. As WMLs are related to stroke and carotid disease, accurate volume measurements are most important. Manual volume computation is laborious, subjective, time consuming, and error prone. Automated methods are a nice alternative since they quantify WML volumes in an objective, efficient, and reliable manner. PVA is initially modeled with a localized edge strength measure since PVA resides in the boundaries between tissues. This map is computed in 3-D and is transformed to a global representation to increase robustness to noise. Significant edges correspond to PVA voxels, which are used to find the PVA fraction α (amount of each tissue present in mixture voxels). Results on simulated and real FLAIR images show high WML segmentation performance compared to ground truth (98.9% and 83% overlap, respectively), which outperforms other methods. Lesion load studies are included that automatically analyze WML volumes for each brain hemisphere separately. This technique does not require any distributional assumptions/parameters or training samples and is applied on a single MR modality, which is a major advantage compared to the traditional methods.     

Fuzzy rule-based signal processing and its application to imagerestoration

A novel signal processing technique based on fuzzy rules is proposed for estimating nonstationary signals, such as image signals, contaminated with additive random noises. In this filter, fuzzy rules concerning the relationship between signal characteristics and filter design are utilized to set the filter parameters, taking the local characteristics of the signal into consideration. The fuzzy rules are found to be quite effective, since the rules to set the filter parameters are usually expressed in an ambiguous style. The high performance of this filter is demonstrated in noise reduction of a 1-D test signal and a natural image with various training signals     

An improved median filter

A modified median filtering technique offering improved smoothing performance while maintaining the edge-preserving ability of the conventional median filter is presented. It is shown how a mean filter outperforms a median filter in noise reduction except on or near the image boundaries. Along with the edge-preserving ability of the median filter, this observation forms the basis of the three-stage filtering algorithm which is described.     

Anisotropic field-of-views in radial imaging

Radial imaging techniques, such as projection-reconstruction (PR), are used in magnetic resonance imaging (MRI) for dynamic imaging, angiography, and short-T(2) imaging. They are robust to flow and motion, have diffuse aliasing patterns, and support short readouts and echo times. One drawback is that standard implementations do not support anisotropic field-of-view (FOV) shapes, which are used to match the imaging parameters to the object or region-of-interest. A set of fast, simple algorithms for 2-D and 3-D PR, and 3-D cones acquisitions are introduced that match the sampling density in frequency space to the desired FOV shape. Tailoring the acquisitions allows for reduction of aliasing artifacts in undersampled applications or scan time reductions without introducing aliasing in fully-sampled applications. It also makes possible new radial imaging applications that were previously unsuitable, such as imaging elongated regions or thin slabs. 2-D PR longitudinal leg images and thin-slab, single breath-hold 3-D PR abdomen images, both with isotropic resolution, demonstrate these new possibilities. No scan time to volume efficiency is lost by using anisotropic FOVs. The acquisition trajectories can be computed on a scan by scan basis.     

Curvature and torsion feature extraction from free-form 3-D meshesat multiple scales

A novel technique is presented for multi-scale curvature computation on a smoothed 3-D surface. This is achieved by convolving local parameterisations of the surface iteratively with 2-D Gaussian filters. In the technique, each vertex of the mesh becomes a local origin around which semi-geodesic co-ordinates are constructed. A geodesic from the origin is first constructed in an arbitrary direction, typically the direction of one of the incident edges. The smoothing eliminates surface noise and slowly erodes small surface detail, resulting in gradual simplification of the object shape. The surface Gaussian and mean curvature values are estimated accurately at multiple scales, together with curvature zero-crossing contours. For better visualisation, the curvature values are then mapped to colours and displayed directly on the surface. Furthermore local maxima of Gaussian and mean curvatures, as well as the torsion maxima of the zero-crossing contours of Gaussian and mean curvatures are also located and displayed on the surface. These features can be utilised by later processes for robust surface matching and object recognition. The technique is independent of the underlying triangulation and is more efficient than volumetric diffusion techniques since 2-D rather than 3-D convolutions are employed. Another advantage is that it is applicable to incomplete surfaces which arise during occlusion or to surfaces with holes     

Motion-based grouping of optical flow fields: the extrapolation andsubtraction technique

The goal of three-dimensional (3-D) motion segmentation is to identify the image areas projected by different moving objects in 3-D space. However, many prevailing methods merely detected the discontinuity of optical flow field and usually considered these boundaries as that produced by different 3-D motions. In fact, the flow discontinuity can be generated either by two different 3-D motions or by the structural discontinuity on the same moving object. The wrong identification causes several problems in 3-D motion estimation. A simple method called the extrapolation and subtraction (ES) technique is proposed to solve these problems. The input image flow field is first partitioned into several functionally analytic regions. Each analytic region is assumed to be projected by a roughly planar patch moving in 3-D space. Based on the parameterization of these analytic flow fields, the ES technique provides a very simple and fast method to test the 3-D motion compatibility between two interested analytic flow fields.     

T2 restoration and noise suppression of hybrid MR images using Wiener and linear prediction techniques

The authors address the problem of enhancing hybrid magnetic resonance (MR) images degraded by T2 effects and additive measurement noise. To reduce imaging time, MR signals are acquired using hybrid imaging (HI) sequences such as rapid acquisition relaxation-enhanced (RARE) and fast spin-echo (FSE). With these techniques, T2 effects act as a distortion filter. This T2 filter affects the signal and results in image spatial resolution and/or contrast loss. Furthermore, the amplitude and phase discontinuities in the T2 filter frequency response function may generate serious ringing artifacts. These distortions will damage image quality and affect object detectability. The authors use the Wiener filter and linear prediction (LP) technique to process HI MR signals in the spatial frequency domain (K-space) and the hybrid domain, respectively. Based on the average amplitude symmetry constraint of the spin echo signal, the amplitude frequency response function of the T2 distortion filter can be estimated and used in the Wiener filter for a global T2 amplitude restoration. Then, the linear prediction technique is utilized to obtain the local signal amplitude and phase estimates around the discontinuities of the frequency response function of the T2 filter. These estimates are used to make local amplitude and phase corrections. The effectiveness of this combined technique in correcting T2 distortion and reducing the measurement noise is analyzed and demonstrated using experiments on both phantoms and human studies.     

Along-Track Focusing of Airborne Radar Sounding Data From West Antarctica for Improving Basal Reflection Analysis and Layer Detection

This paper presents focused synthetic aperture radar (SAR) processing of airborne radar sounding data acquired with the High-Capability Radar Sounder system at 60 MHz. The motivation is to improve basal reflection analysis for water detection and to improve layer detection and tracking. The processing and reflection analyses are applied to data from Kamb Ice Stream, West Antarctica. The SAR processor correlates the radar data with reference echoes from subsurface point targets. The references are 1-D responses limited by the pulse nadir footprint or 2-D responses that include echo tails. Unfocused SAR and incoherent integration are included for comparison. Echoes are accurately preserved from along-track slopes up to about 0.5deg for unfocused SAR, 3deg for 1-D correlations, and 10deg for 2-D correlations. The noise/clutter levels increase from unfocused SAR to 1-D and 2-D correlations, but additional gain compensates at the basal interface. The basal echo signal-to-noise ratio improvement is typically about 5 dB, and up to 10 dB for 2-D correlations in rough regions. The increased noise degrades the clarity of internal layers in the 2-D correlations, but detection of layers with slopes greater than 3deg is improved. Reflection coefficients are computed for basal water detection, and the results are compared for the different processing methods. There is a significant increase in the detected water from unfocused SAR to 1-D correlations, indicating that substantial basal water exists on moderately sloped interfaces. Very little additional water is detected from the 2-D correlations. The results from incoherent integration are close to the focused SAR results, but the noise/clutter levels are much greater.     

小波域中的双边滤波

本文首先介绍了小波域中几种常用的滤波方法,分析了双边滤波的性质和特点,在此基础上提出了小波域中的双边滤波.这种方法充分利用了小波分析的时频局部化和多分辨率的特点,使滤波结果的信噪比较高的同时边缘具有较小的模糊.本文的实验结果说明该方法是行之有效的.     

Lnear Phase FIR Filter on Measuring 3—D Surface

An optical technology for 3-D surface measurement is se up.The technology,based on a deformed projected grating pattern which carries the 3-D information of the measured object,can automatically and accurately obtain the phase map of a measured object by using a linear-phase FIR filter.In contrast to the 2-D fast Fourier transform technique,it‘s more than fast.Only one image pattern is sufficient for measuring .The phase map can be processed without assigning fringe orders and making distinction between a depression and an elevation.Theoretical analysis and experimental result are presented.     

Linear Phase FIR Filter on Measuring 3-D Surface

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Separation of image parts using 2-D parallel form recursive filters

This correspondence deals with a new technique to separate objects or image parts in a composite image. A parallel form extension of a 2-D Steiglitz-McBride method is applied to the discrete cosine transform (DCT) of the image containing the objects that are to be separated. The obtained parallel form is the sum of several filters or systems, where the impulse response of each filter corresponds to the DCT of one object in the original image. Preliminary results on an image with two objects show that the algorithm works well, even in the case where one object occludes another as well as in the case of moderate noise.     

Scale-based diffusive image filtering preserving boundary sharpnessand fine structures

Image acquisition techniques often suffer from low signal-to-noise ratio (SNR) and/or contrast-to-noise ratio (CNR). Although many acquisition techniques are available to minimize these, post acquisition filtering is a major off-line image processing technique commonly used to improve the SNR and CNR. A major drawback of filtering is that it often diffuses/blurs important structures along with noise. In this paper, we introduce two scale-based filtering methods that use local structure size or "object scale" information to arrest smoothing around fine structures and across even low-gradient boundaries. The first of these methods uses a weighted average over a scale-dependent neighborhood while the other employs scale-dependent diffusion conductance to perform filtering. Both methods adaptively modify the degree of filtering at any image location depending on local object scale. Object scale allows us to accurately use a restricted homogeneity parameter for filtering in regions with fine details and in the vicinity of boundaries while a generous parameter in the interiors of homogeneous regions. Qualitative experiments based on both phantoms and patient magnetic resonance images show significant improvements using the scale-based methods over the extant anisotropic diffusive filtering method in preserving fine details and sharpness of object boundaries. Quantitative analyses utilizing 25 phantom images generated under a range of conditions of blurring, noise, and background variation confirm the superiority of the new scale-based approaches.     

A novel 5-D depth–velocity filter for enhancing noisy light field videos

A 5-D depth–velocity filter is proposed for enhancing moving objects in noisy light field videos (LFVs) (also known as plenoptic videos). The proposed filter consists of an ultra-low complexity 5-D IIR depth filter and a 5-D FIR velocity filter. The 5-D IIR depth filter is employed to denoise a noisy LFV. The denoised LFV is then utilized to estimate the 3-D apparent velocity of the moving object of interest. The 5-D FIR velocity filter is designed based on the estimated 3-D apparent velocity and is used to enhance the moving object of interest while attenuating other interfering moving objects. Experimental results confirm the effectiveness of the proposed 5-D depth–velocity filter compared to previously reported 5-D depth–velocity filters.     

Fan filters, the 3-D Radon transform, and image sequence analysis

This paper develops a theory for the application of fan filters to moving objects. In contrast to previous treatments of the subject based on the 3-D Fourier transform, simplicity and insight are achieved by using the 3-D Radon transform. With this point of view, the Radon transform decomposes the image sequence into a set of plane waves that are parameterized by a two-component slowness vector. Fan filtering is equivalent to a multiplication in the Radon transform domain by a slowness response function, followed by an inverse Radon transform. The plane wave representation of a moving object involves only a restricted set of slownesses such that the inner product of the plane wave slowness vector and the moving object velocity vector is equal to one. All of the complexity in the application of fan filters to image sequences results from the velocity-slowness mapping not being one-to-one; therefore, the filter response cannot be independently specified at all velocities. A key contribution of this paper is to elucidate both the power and the limitations of fan filtering in this new application. A potential application of 3-D fan filters is in the detection of moving targets in clutter and noise. For example, an appropriately designed fan filter can reject perfectly all moving objects whose speed, irrespective of heading, is less than a specified cut-off speed, with only minor attenuation of significantly faster objects. A simple geometric construction determines the response of the filter for speeds greater than the cut-off speed.     

An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, , 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, , 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, , 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented.     

Efficient representation of object shape for silhouetteintersection

A new shape representation-the radial intersection set (RIS)-is presented. The RIS is an object-centred model in which 2-D and 3-D boundaries are represented via their intersection with radial lines from some specific origin. The RIS representation allows efficient 3-D reconstruction using silhouette intersection from an arbitrary number of 2-D perspective views. The relationship between the visual hull (Laurentini, 1994) of the 3-D object and the silhouettes it may generate is defined as a set intersection operator. This operator allows the direct generation of 3-D RIS models from silhouettes. The RIS method is shown to compare favourably, in terms of both speed and storage, with existing octree techniques. Examples of images rendered from RIS models are presented. These show high visual fidelity