The purpose of this study is to develop an automatic technique to segment the membrane of the maxillary sinus with morphological changes (e.g. thickened membrane and cysts) for the detection of abnormalities. The first step is to segment the sinus bone cavity in the CBCT image using fuzzy C-mean algorithm. Then, the vertices of inner bone walls of sinus in the mesh model are screened with vertex normal direction and angular based mean-distance filtering. The resulted vertices are then used to generate the bony sinus cavity mesh model by using Poisson surface reconstruction. Finally, the sinus membrane morphological changes are segmented by subtracting the air sinus segmentation from the reconstructed bony sinus cavity. The proposed method has been applied on 5 maxillary sinuses with mucosal thickening and has demonstrated that it can segment thin membrane thickening (<; 2 mm) successfully within 4.1% and 3.5% error in volume and surface area respectively. Existing methods have issues of leakages at openings and thin bones, and inaccuracy with irregular contours commonly seen in maxillary sinus. The current method overcomes these shortcomings.

3D digital smile design (DSD) gains great interest in dentistry because it enables esthetic design of teeth and gum. However, the color texture of teeth and gum is often lost/distorted in the digitization process. Recently, the image-to-geometry registration shade mapping (IGRSM) method was proposed for registering color texture from 2D photography to 3D mesh model. It allows better control of illumination and color calibration for automatic teeth shade matching. In this paper, we investigate automated techniques to find the correspondences between 3D tooth model and color intraoral photographs for accurately perform the IGRSM. We propose to use the tooth cusp tips as the correspondence points for the IGR because they can be reliably detected both in 2D photography and 3D surface scan. A modified gradient descent method with directional priority (GDDP) and region growing are developed to find the 3D correspondence points. For the 2D image, the tooth tips contour lines are extracted based on luminosity and chromaticity, the contour peaks are then detected as the correspondence points. From the experimental results, the proposed method shows excellent accuracy in detecting the correspondence points between 2D photography and 3D tooth model. The average registration error is less than 15 pixels for 4752×3168 size intraoral image.

A reliable estimator for speech presence probability (SPP) can significantly improve the performance of many speech enhancement algorithms. Previous work showed that a good SPP estimator can be obtained by using a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. In this paper, a wavelet based denoising algorithm is proposed for such purpose. We first apply the wavelet transform to the periodogram of a noisy speech signal to generate an oracle for indicating the locations of the noise floor in the periodogram. We then make use of that oracle to selectively remove the wavelet coefficients of the noise floor in the log multitaper spectrum (MTS) of the noisy speech. The remaining wavelet coefficients are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables a significantly improvement in the quality and intelligibility of the enhanced speeches.

Traditional Fourier transform profilometry (FTP) removes the zero spectrum of the acquired fringe image using the linear filtering approach. Such approach assumes there is no aliasing between the zero spectrum and the higher harmonics of the image, which however is not true in general. Thus it cannot adapt to sharp illuminant changes in the fringe image. One practical solution is to exploit one more fringe pattern with π phase shift to eliminate the zero spectrum. This however complicates the system in that the projection of the fringe patterns should be synchronized with the capturing device. Motion or imperfect synchronization makes the subsequent 3D reconstruction erroneous. In this paper, we introduce a 2D joint bilateral filter to help in removing the zero spectrum. Since the bilateral filter is sensitive to abrupt changes in the fringe image, we can accurately extract the zero spectrum even if there are sharp spatial variations in the pixel magnitude. Experimental results show that the proposed algorithm is superior to the traditional methods and facilitate accurate reconstruction of objects' 3D model.

In optical phase shift profilometry, parallel fringe patterns are projected onto an object and the deformed fringes are captured using a digital camera. It is of particular interest because it enables reconstruction of the 3D shape of the object using just a few image captures, which facilitates real time applications. However, when using the approach in real life environment, it is noticed that the noise in the captured images can greatly affect the reconstruction quality. In this paper, we firstly analyze why the noisy fringe images can best be analyzed using the oriented 2D dual tree complex wavelet transform. We then suggest an effective yet simple method for enhancing the noisy fringe images. Both the simulation and experiment results show that the new approach can give good performance in reconstruction with fringe images even at high noise level.

To obtain a reliable estimate of the a-priori signal to noise (SNR) ratio is crucial to most frequency domain speech enhancement algorithms. Recently, the low variance multitaper spectrum (MTS) estimator with wavelet denoising was suggested for the estimation of the a-priori SNR However, traditional approach directly plugs in the wavelet shrinkage denoiser and adopts the universal threshold which is not fully optimized to the characteristic of the MTS of noisy signals. In this paper, a two-stage estimation algorithm is proposed. First, the log MTS components that are dominated by noise are detected and removed in the wavelet domain. Second, a modified SUREshrink scheme is applied to further remove the noise remained in the speech spectral peaks. The new estimator is applied to the traditional Wiener filter and log MMSE speech enhancement algorithms and leads to significantly better performance.

Classical speech enhancement algorithms often require a good estimation of the short-time power spectrum using, for instance, the periodogram methods. However, it is well known that traditional periodogram methods are prone to induce large variance, hence produces the “musical noise” after enhancement. To alleviate this problem, multitaper spectrum (MTS) estimators with wavelet denoising were proposed. In this paper, we investigate the properties of the MTS of noisy speech signals. We find that, in the log MTS domain, the variance of noise varies according to the magnitude of the underlying speech spectrum. It implies that when applying wavelet denoising to the log MTS, the constant threshold used in the traditional methods is not appropriate. Based on this observation, we further develop a wavelet denoising method with adaptive threshold for estimating power spectrum using multitaper. Simulation results show that the spectrum estimated using the proposed method is consistently more accurate than the traditional uniform thresholding methods. Hence, it further improves the current speech enhancement algorithms using the MTS approaches.

The Generalized Sidelobe Canceller (GSC) efficiently realizes the optimal Linearly Constrained Minimum Variance (LCMV) beamformer and delivers excellent beamforming results by reducing directional interference and noise. However, when the input signals are contaminated by other type of noises, such as background and diffused noises, the overall performance of GSC can be even worse than the traditional delay-and-sum beamformers. In this paper, we investigate the application of the spatially adaptive multiwavelet (MWT) denoising technique to the GSC in an environment with severe diffused noise. Comparing with the traditional scalar wavelets, the multiwavelets can better characterize the noise information in the signal such that better denoising performance can be achieved. Different approaches for integrating the GSC and the multiwavelet denoiser were studied. It is found that by adding two denoisers, one to the fixed constrained part and another to the final GSC output, an improvement of 2dB in SNR can be achieved as compared with the traditional GSC method.

Prefilters are generally applied to the discrete multiwavelet transform (DMWT) for processing scalar signals. To fully utilize the benefit given by the multiwavelets, we have recently shown a maximally decimated orthogonal prefilter which preserves the linear phase property and the approximation power of the multiwavelets. However, such design requires the point of symmetry of each channel of the prefilter to match with the scaling functions of the target multiwavelet system. A compatible filter bank structure can be very difficult to find or simply does not exist, e.g. for multiplicity 2 multiwavelets. In this paper, we suggest a new DMWT structure in which the prefilter is combined with the first stage of DMWT. The advantage of the new structure is twofold: First, the computational complexity can be greatly reduced. Second, additional design freedom allows maximally decimated, orthogonal and symmetric prefilters even for low multiplicity. We evaluated the computational complexity and energy compaction capability of the new DMWT structure. Satisfactory results are obtained in comparing with the traditional approaches.

Traditional design of critically sampled prefilters for discrete multiwavelet transform ignores the preservation of the linear phase property, which is important for many applications, such as image coding and digital communications. Balanced multiwavelets solve this problem but make the filters longer. By using linear phase filter banks, we propose a simple algorithm for the design of orthogonal symmetric prefilter banks that can be used with the discrete multiwavelet transform. The prefilter bank resulted is orthogonal and critically sampled and can preserve the approximation power of the multiwavelet as well as the linear phase property. Experimental results show that the systems using the proposed symmetric prefilter banks give better performance as compared with using nonlinear phase prefilters.

Recent research found that multivariate shrinkage on multiwavelet transform coefficients further improves the traditional wavelet methods. It is because the multiwavelet transform, with appropriate initialization, provides better representation of signals so that their difference from noise can be clearly identified. In this paper, we consider the optimal threshold selection for multiwavelet denoising by using a multivariate shrinkage function. Firstly, we study the threshold selection using the Stein's unbiased risk estimator (SURE) for each resolution level when the noise structure is given. Then, we consider the method of generalized cross validation (GCV) when the noise structure is not known a priori. Simulation results show that the higher multiplicity (>2) wavelets usually give better denoising results. Besides, the proposed threshold estimators often suggest better thresholds as compared with the traditional estimators.

Traditional multiwavelet shrinkage denoising techniques require a priori knowledge of noise variance that may not be obtained in some practical situations. By using generalized cross validation (GCV), we propose in this paper a new level-dependent risk estimator for multiwavelet shrinkage that does not require such a priori information. Simulation results verify that the resulted risk estimator gives better indication on threshold selection comparing with the traditional GCV method. Improved denoising performance is then achieved particularly for higher multiplicity multiwavelet shrinkage.

Restoring an image from its convolution with an unknown blur function is a well-known ill-posed problem in image processing. Many approaches have been proposed to solve the problem and they have shown to have good performance in identifying the blur function and restoring the original image. However, in actual implementation, various problems incurred due to the large data size and long computational time of these approaches are undesirable even with the current computing machines. In this paper, an efficient algorithm is proposed for blind image restoration based on the discrete periodic Radon transform (DPRT). With DPRT, the original two-dimensional blind image restoration problem is converted into one-dimensional ones, which greatly reduces the memory size and computational time required. Experimental results show that the resulting approach is faster in almost an order of magnitude as compared with the traditional approach, while the quality of the restored image is similar.

This paper describes the performance of the MPEG-4 still texture image codec in coding noisy images. As will be shown, when using the MPEG-4 still texture image codec to compress a noisy image, increasing the compression rate does not necessarily imply reducing the peak-signal-to-noise ratio (PSNR) of the decoded image. An optimal operating point having the highest PSNR can be obtained within the low bit rate region. Nevertheless, the visual quality of the decoded noisy image at this optimal operating point is greatly degraded by the so-called "cross" shape artifact. In this paper, we analyze the reason for the existence of the optimal operating point and the "cross" shape artifact when using the MPEG-4 still texture image codec to compress noisy images. We then propose an adaptive thresholding technique to remove the "cross" shape artifact of the decoded images. It requires only a slight modification to the quantization process of the traditional MPEG-4 encoder while the decoder remains unchanged. Finally, an analytical study is performed for the selection and validation of the threshold value used in the adaptive thresholding technique. It is shown that, the visual quality and PSNR of the decoded images are much improved by using the proposed technique comparing with the traditional MPEG-4 still texture image codec in coding noisy images.

An adaptive regularity scalable wavelet image coding algorithm is proposed in this paper. The bitstream is generated in the order of regularity such that visually more important components of a decoded image can be obtained first, followed by the higher regularity components as more bits are received and decoded. The scalability is achieved by selecting different extents of wavelet coefficients at various regularity levels. These regularity levels are determined adaptively. Regularity of the image is estimated from the interscale ratios of the separable wavelet transform magnitude sums. Compared to the wavelet image coder generating resolution scalable bitstreams, significant improvement in visual quality and PSNR can be achieved.

In this paper we consider the optimization of the boundary filters with perfect reconstruction property and moment conditions for multiwavelet, Since the filters coefficients are matrices, the moment conditions for multiwavelet do not relate to the filters coefficients in a simple way. We propose a scheme to formulate the moment conditions for the optimization of a given multiwavelet. We apply the proposed scheme on GHM multiwavelet and obtain a set of boundary filters, which possess the desired moment conditions and reduce the boundary artifacts up to 75% as compared to the truncated filters.

We explore the wavelet coefficient selection and denoising by singularity detection (SD) for the embedded zero-tree wavelet (EZW) coding algorithm in this paper. The EZW coding algorithm exploits the relation between the multi-scale wavelet coefficients that finer scale wavelet coefficients are probably to vanish if the coarse scale wavelet coefficient vanishes. It is true for the parts of image that are regular but not the cases for noise-like features. In other words, the performance of the coding algorithm may be greatly degraded for the latter. In this paper, we investigate to arrange the wavelet coefficients according to the local regularity, by using the computed wavelet coefficients from the encoding filters. The advantage is two folds. For normal coding, we can make the encoded bit-stream first appear with the wavelet coefficients that correspond to the most regular part of the image, and the irregular one's follows. For noisy image encoding, we can remove noises before encoding hence increase the image quality as well as the coding efficiency.

The blocking effect is considered as the most disturbing artifact of JPEG decoded images. Many researchers have suggested various methods to tackle this problem. The wavelet transform modulus maxima (WTMM) approach was proposed which gives a significant improvement over the previous methods in terms of the signal-to-noise ratio and visual quality. However, the WTMM deblocking algorithm is an iterative algorithm that requires a long computation time to reconstruct the processed WTMM to obtain the deblocked image. A new wavelet based algorithm for JPEG image deblocking is proposed. The new algorithm is based on the idea that, besides using the WTMM, the singularity of an image can also be detected by computing the sums of the wavelet coefficients inside the so-called "directional cone of influence" in different scales of the image. The new algorithm has the advantage as the WTMM approach that it can effectively identify the edge and the smooth regions of an image irrespective of the discontinuities introduced by the blocking effect. It is an improvement over the WTMM approach in that only a simple inverse wavelet transform is required to reconstruct the processed wavelet coefficients to obtain the deblocked image. As the WTMM approach, the new algorithm gives consistent and significant improvement over the previous methods for JPEG image deblocking.

In this work, we introduce a deblocking algorithm for Joint Photographic Experts Group (JPEG) decoded images using the wavelet transform modulus maxima (WTMM) representation. Under the WTMM representation, we can characterize the blocking effect of a JPEG decoded image as: 1) small modulus maxima at block boundaries oversmooth regions; 2) noise or irregular structures near strong edges; and 3) corrupted edges across block boundaries. The WTMM representation not only provides characterization of the blocking effect, but also enables simple and local operations to reduce the adverse effect due to this problem. The proposed algorithm first performs a segmentation on a JPEG decoded image to identify the texture regions by noting that their WTMM have small variation in regularity. We do not process the modulus maxima of these regions, to avoid the image texture being "oversmoothed"by the algorithm. Then, the singularities in the remaining regions of the blocky image and the small modulus maxima at block boundaries are removed. We link up the corrupted edges, and regularize the phase of modulus maxima as well as the magnitude of strong edges. Finally,the image is reconstructed using the projection onto convex set (POCS)technique on the processed WTMM of that JPEG decoded image.This simple algorithm improves the quality of a JPEG decoded image inthe senses of signal-to-noise ratio (SNR) as well as visual quality. We also compare the performance of our algorithm to the previous approaches,such as CLS and POCS methods. The most remarkable advantage of the WTMM deblocking algorithm is that we can directly process the edges and texture of an image using its WTMM representation.

The wavelet localization technique has been applied to region-of-interest tomography. It achieves a significant saving in the required projections if only a small region of a tomographic image is of interest. We first show that, with the same sampling requirement, a simple interpolation scheme applied on the samples can give a result at least as good as that achieved by using the wavelet localization approach. It means that we can use a much simpler approach to achieve the same performance. Second, we propose a new sampling scheme such that the required projections of each angle are further reduced in a multiresolution form. With this sampling scheme, more than 84% of projections are saved to reconstruct a 32/spl times/32 pixels region of a 256/spl times/256 pixels image. The signal-to-error ratio of the reconstructed region-of-interest is over 50 dB as compare with the case of full projection. Moreover, we also investigate the effect of applying the interlaced sampling scheme on the proposed method. It is seen that a further reduction in the sampling requirement can be achieved although a slight decrease in signal-to-error ratio may result.

In this paper, we introduce a local deblocking algorithm for JPEG decoded images using the wavelet transform modulus maxima (WTMM) representation. Under the WTMM representation, we can characterize the blocking effect as: 1) small modulus maxima at block boundaries over smooth regions; 2) noises or irregular structures near strong edges; 3) corrupted edges across block boundaries. The WTMM representation not only provides characterization of the blocking effect, but also enables simple and local operations on those singularities. The proposed algorithm first performs a segmentation to discriminate the texture regions of an image based on the WTMM local regularity variance. We then keep the modulus maxima of these regions, which are of low regularity variation, unchange to avoid the image texture being "over-smoothed" by the algorithm. Then, the singularities on the remaining regions of the blocky image and small modulus maxima at block boundaries are removed. Then, we link up the corrupted edges and regularize the phase of modulus maxima as well as the amplitude of strong edges. Finally, the image is reconstructed using the projection onto convex sets (POCS) technique on the processed WTMM of the JPEG decoded image. This simple algorithm improves the quality of JPEG decoded image in the sense of signal to noise ratio as well as visual quality. We also compare the performance of our algorithm with the previous approaches and show the superiority over them. The most remarkable advantage of the WTMM deblocking algorithm is that it incorporates direct edges and texture operations into the WTMM representation.

Presents a fast algorithm for the computation of the wavelet transform in higher dimensional Euclidean space R/sup n/ with arbitrary shaped wavelets. The algorithm is a direct consequence of the convolution property of the Radon transform and shows significant improvement in speed. The authors also present a novel approach for the computation of the Daubechies type wavelet transform under the Radon transform domain where the n-dimensional multiresolution analysis (MRA) is reduced to one-dimensional MRA. They found applications of this approach on, for instance, multiresolution reconstruction of a tomographic image with the standard methods of denoising, where determination of wavelet coefficients is required under the Radon transform domain. Along with the possibility of reducing samples angularly with decreasing resolution, the efficiency can be further improved. Also, extra properties such as the "rotated" wavelet can be easily implemented with this algorithm.