Surface alignment of an elastic body using a multiresolution wavelet representation

An algorithm for nonlinear registration of an elastic body is developed. Surfaces (outlines) of known anatomic structures are used to align all other (internal) points. The deformation field is represented with a multiresolution wavelet expansion and is modeled by the partial differential equations of linear elasticity. A hierarchical approach that reduces algorithm complexity is adopted. The performance of the algorithm is evaluated by two-dimensional alignment of sections from mouse brains located in the olfactory bulbs. The registration algorithm was guided by manually delineated contours of a subset of brain structures and validated based on another subset of brain structures. The wavelet alignment algorithm produced a twofold to fivefold improvement in accuracy over an affine (linear) alignment algorithm.     

Resolution enhancement of monochrome and color video using motioncompensation

We propose an iterative algorithm for enhancing the resolution of monochrome and color image sequences. Various approaches toward motion estimation are investigated and compared. Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since the original high-resolution images are not available. Improved motion estimates may be obtained by using a more robust and accurate motion estimator, such as a pel-recursive scheme instead of block matching, in processing color image sequences, there is the added advantage of having more flexibility in how the final motion estimates are obtained, and further improvement in the accuracy of the motion field is therefore possible. This is because there are three different intensity fields (channels) conveying the same motion information. In this paper, the choice of which motion estimator to use versus how the final estimates are obtained is weighed to see which issue is more critical in improving the estimated high-resolution sequences. Toward this end, an iterative algorithm is proposed, and two sets of experiments are presented. First, several different experiments using the same motion estimator but three different data fusion approaches to merge the individual motion fields were performed. Second, estimated high-resolution images using the block matching estimator were compared to those obtained by employing a pel-recursive scheme. Experiments were performed on a real color image sequence, and performance was measured by the peak signal to noise ratio (PSNR).     

Signal decomposition of transmembrane voltage-sensitive dye fluorescence using a multiresolution wavelet analysis

Fluorescence imaging of transmembrane voltage-sensitive dyes is used to study electrical activation in cardiac tissue. However, the fluorescence signals, typically, have low SNRs and may be contaminated with motion artifact. In this report, we introduce a new processing approach for fluoresced transmembrane potentials (fTmps) that is based upon a discrete wavelet transform. We show how fTmp signals can be decomposed and reconstructed to form three subsignals that contain signal noise (noise signal), the early depolarization phase of the action potential (rTmp signal), and motion artifact (rMA signal). A coiflet4 wavelet is used for fTmp decomposition and reconstruction of these subsignals. Results using fTmp signals that are contaminated with motion artifact indicate that the approach is a useful processing step to remove baseline drift, reduce noise, and reveal wavefronts. It streamlines the preprocessing of fTmps for the subsequent measurement of activation times and conduction velocities. It is a promising approach for studying wavefronts without aggressive mechanical tissue constraint or electromechanical uncoupling agents and is, useful for single-camera systems that do not provide for ratiometric imaging.     

基于小波多尺度表示的图像匹配研究

根据小波变换系数对信号平移步长的变化规律,提出一种基于小波金字塔结构的、遍历式的图像匹配方法,这种方法消除了由于小波变换对平移的敏感性所引起的误匹配,在匹配策略上,以小波分解高频分量的匹配为主。实验证实,本匹配方法对实时图和参考图的局部灰度反转不敏感,具有一定抗几何失真的能力,优于经典的灰度相关匹配。     

Hiding digital watermarks using multiresolution wavelet transform

In this paper, an image accreditation technique by embedding digital watermarks in images is proposed. The proposed method for the digital watermarking is based on the wavelet transform. This is unlike most previous work, which used a random number of a sequence of bits as a watermark and where the watermark can only be detected by comparing an experimental threshold value to determine whether a sequence of random signals is the watermark. The proposed approach embeds a watermark with visual recognizable patterns, such as binary, gray, or color image in images by modifying the frequency part of the images. In the proposed approach, an original image is decomposed into wavelet coefficients. Then, multi-energy watermarking scheme based on the qualified significant wavelet tree (QSWT) is used to achieve the robustness of the watermarking. Unlike other watermarking techniques that use a single casting energy, QSWT adopts adaptive casting energy in different resolutions. The performance of the proposed watermarking is robust to a variety of signal distortions, such as JPEG, image cropping, sharpening, median filtering, and incorporating attacks     

Multiscale curvature-based shape representation using B-splinewavelets

This paper presents a new multiscale curvature-based shape representation technique with application to curve data compression using B-spline wavelets. The evolution of the curve is implemented in the B-spline scale-space, which enjoys a number of advantages over the classical Gaussian scale-space, for instance, the availability of fast algorithms. The B-spline wavelet transforms are used to efficiently estimate the multiscale curvature functions. Based on the curvature scale-space image, we introduce a coarse-to-fine matching algorithm which automatically detects the dominant points and uses them as knots for curve interpolation.     

Rotation-invariant multiresolution texture analysis using radon and wavelet transforms.

A new rotation-invariant texture-analysis technique using Radon and wavelet transforms is proposed. This technique utilizes the Radon transform to convert the rotation to translation and then applies a translation-invariant wavelet transform to the result to extract texture features. A kappa-nearest neighbors classifier is employed to classify texture patterns. A method to find the optimal number of projections for the Radon transform is proposed. It is shown that the extracted features generate an efficient orthogonal feature space. It is also shown that the proposed features extract both of the local and directional information of the texture patterns. The proposed method is robust to additive white noise as a result of summing pixel values to generate projections in the Radon transform step. To test and evaluate the method, we employed several sets of textures along with different wavelet bases. Experimental results show the superiority of the proposed method and its robustness to additive white noise in comparison with some recent texture-analysis methods.     

Multiscale corner detection by using wavelet transform

A multiscale corner detection algorithm based on the wavelet transform of contour orientation is proposed. It can utilize both the information of the local extrema and modulus of transform results to detect corners and arcs effectively. The ramp-width of contour orientation profile, which can be computed using the transformed modulus of two scales, reveals the difference between corner and arc and is utilized in the determination of corner points. The experimental results have shown that the detector is more effective than both the single- and multiple-scale detectors. They also demonstrate that the detector is insensitive to boundary noise. In addition, the proposed method is more efficient than the other multiscale corner detector because it operates on fewer number of scales, which can be implemented by a fast transform algorithm.     

Multiscale texture classification using reduced quaternion wavelet transform

This article proposes a study of the reduced quaternion wavelet transform (RQWT) which has one shift-invariant magnitude and three angle phases at each scale from digital image analysis application. A new multiscale texture classifier which uses features extracted from the sub-bands of the RQWT decomposition is proposed in the transform domain. The proposed method can achieve a high texture classification rate. The experimental results can demonstrate the robustness of the proposed method and achieve a higher texture classification accuracy rate than a famous wavelet transform based classifier.     

Multifocus and multispectral image fusion based on pixel significance using multiresolution decomposition

Image fusion has been receiving increasing attention in the research community with the aim of investigating general formal solutions to a wide spectrum of applications. The objective of this work is to formulate a method that can efficiently fuse multifocus as well as multispectral images for context enhancement and thus can be used by different applications. We propose a novel pixel fusion rule based on multiresolution decomposition of the source images using wavelet, wavelet-packet, and contourlet transform. To compute fused pixel value, we take weighted average of the source pixels, where the weight to be given to the pixel is adaptively decided based on the significance of the pixel, which in turn is decided by the corresponding children pixels in the finer resolution bands. The fusion performance has been extensively tested on different types of images viz. multifocus images, medical images (CT and MRI), as well as IR ? visible surveillance images. Several pairs of images were fused to compare the results quantitatively as well as qualitatively with various recently published methods. The analysis shows that for all the image types into consideration, the proposed method increases the quality of the fused image significantly, both visually and quantitatively, by preserving all the relevant information. The major achievement is average 50% reduction in artifacts.     

VBR视频流量的小波包分解及其长时预测

长时预测是VBR视频流量预测领域中的难点问题.针对其时变、非线性以及长相关性等特点,提出一种多尺度分解的VBR视频业务的特征提取方法.选择具有任意多分辨分解特性的小波包,对其进行空间划分并求解适合视频信号特征提取的最优分解基.基于最优基对视频信号进行快速多尺度分解,得到了各级节点的小波系数矩阵,建立了基于最小二乘支持向量机与最小均方的小波系数预测方法.最后,根据预测小波系数,进一步提出了基于小波系数逆变换的视频流量长时预测方法.仿真结果验证了此算法的有效性.     

Group-based spatio-temporal video analysis and abstraction using wavelet parameters

In this paper, we present a spatio-temporal event-based approach to video signal analysis and abstraction employing wavelet transform features. The video signal is assumed to be a sequence of overlapping independent visual components called events, which typically are temporally overlapping compact functions that describe temporal evolution of a given set of the spatial parameters of the video signal. We utilize event-based temporal decomposition technique to resolve the overlapping arrangement of the video signal that is known to be one of the main concerns in video analysis via conventional frame-based schemes. In our method, a set of spatial parameters, extracted from the video, is expressed as a linear combination of a set of temporally overlapping compact functions, called events, through an optimization process. First, to reduce computational complexity, the video sequence is divided into overlapped groups. Next, Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial parameters. Temporal decomposition is then applied to the GGD parameters, structured as a frame-based matrix of GGD vectors, to compute the event functions and associated orthogonal GGD parameters. Frames located at event centroids, which are much smaller in number than the number of frames in the original video, are taken as candidates for the keyframes that are selected based on a distance criterion in the feature space. Our contribution is that this still image video abstraction scheme does not need shot or cluster boundary detection, unlike current methods. Experimental results confirm the efficiency and accuracy of our approach.     

An image multiresolution representation for lossless and lossycompression

We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integer addition and bit-shift operations. During its calculation, the number of bits required to represent the transformed image is kept small through careful scaling and truncations. Numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity. In addition, we propose entropy-coding methods that exploit the multiresolution structure, and can efficiently compress the transformed image for progressive transmission (up to exact recovery). The lossless compression ratios are among the best in the literature, and simultaneously the rate versus distortion performance is comparable to those of the most efficient lossy compression methods.     

Scalable wavelet video coding using aliasing-reduced hierarchicalmotion compensation

We describe a spatially scalable video coding framework in which motion correspondences between successive video frames are exploited in the wavelet transform domain. The basic motivation for our coder is that motion fields are typically smooth and, therefore, can be efficiently captured through a multiresolutional framework. A wavelet decomposition is applied to each video frame and the coefficients at each level are predicted from the coarser level through backward motion compensation. To remove the aliasing effects caused by downsampling in the transform, a special interpolation filter is designed with the weighted aliasing energy as part of the optimization goal, and motion estimation is carried out with low pass filtering and interpolation in the estimation loop. Further, to achieve robust motion estimation against quantization noise, we propose a novel backward/forward hybrid motion compensation scheme, and a tree structured dynamic programming algorithm to optimize the backward/forward mode choices. A novel adaptive quantization scheme is applied to code the motion predicted residue wavelet coefficients, Experimental results reveal 0.3-2-dB increase in coded PSNR at low bit rates over the state-of-the-art H.263 standard with all enhancement modes enabled, and similar improvements over MPEG-2 at high bit rates, with a considerable improvement in subjective reconstruction quality, while simultaneously supporting a scalable representation.     

Low bit-rate video coding using wavelet vector quantisation

A method for low bit-rate video coding based on wavelet vector quantisation is proposed. Motion estimation/compensation using overlapped block matching (OBM) is employed to eliminate the blocking effects in the prediction error introduced by conventional block matching. It is shown that OBM significantly increases the efficiency of the wavelet transform coder. The motion-compensated interframe prediction error is decomposed using a wavelet transform and a method is employed for the efficient coding of the wavelet coefficients. In this technique, the coefficients are coded with a zero-tree multistage lattice vector quantiser. Simulation results are provided to evaluate the coding performance of the described coding scheme for low bit-rate video coding. It provides constant bit rate, obviating the need for buffer, with just small fluctuations in PSNR. Moreover, comparison with the RM8 implementation of the standard H261 video coder shows that the presented codec provides improvements in both peak signal-to-noise ratio and picture quality     

Digital image watermarking based on multiresolution decomposition

A multiresolution watermarking method with grey-level watermarking is presented. Both the watermark and the original image are decomposed into three-level multiresolution structures using different mechanisms. The decomposed watermark information is further coded by error correction coding. Each level of watermark information is embedded into the corresponding level of the transformed image. Experimental results show that the proposed algorithm is robust against common image manipulations     

Three-dimensional wavelet transform video coding using symmetriccodebook vector quantization

A new vector quantizer codebook design for video compression is described. This uses the notion that symmetries in the data, which are seldom captured exactly in any training dataset, are important perceptually and lead to a more robust codebook. The method is illustrated using three-dimensional (3-D) wavelet transformed video sequences.     

Digital image decomposition and contrast enhancement using high-dimensional model representation

Contrast is the difference in brightness and color that makes an object distinguishable. Contrast enhancement (CE) is a technique used to improve the visual quality of an image for human recognition. This study proposes a new methodology called high-dimensional model representation (HDMR) for enhancing contrast in digital images. The novelty of HDMR is that the method first decomposes the image into its dimensions, then represents the image using the superposition of decomposed components and finally enhances contrast in the image by adding certain HDMR components to the representation. HDMR has high performance as a CE technique in both grayscale and color images when compared with some state-of-the-art methods.     

Closed-form connectivity-preserving solutions for motioncompensation using 2-D meshes

Motion compensation using two-dimensional (2-D) mesh models requires computation of the parameters of a spatial transformation for each mesh element (patch). It is well known that the parameters of an affine (bilinear or perspective) mapping can be uniquely estimated from three (four) point correspondences (at the vertices of a triangular or quadrilateral mesh element). On the other hand, overdetermined solutions using more than the required minimum number of point correspondences provide increased robustness against correspondence-estimation errors, however, this necessitates special consideration to preserve mesh-connectivity. This paper presents closed-form, overdetermined solutions for least squares estimation of affine motion parameters for a triangular mesh, which preserve mesh-connectivity using patch-based or node-based connectivity constraints. In particular, four new algorithms are presented: patch-constrained methods using point correspondences or spatio-temporal intensity gradients, and node-constrained methods using point correspondences or spatio-temporal intensity gradients. The methods using point correspondences can be viewed as postprocessing of a dense motion field for best representation in terms of a set of irregularly spaced samples. The methods that are based on spatio-temporal intensity gradients offer closed-form solutions for direct estimation of the best node-point motion vectors (equivalently the best transformation parameters). We show that the performance of the proposed closed-form solutions are comparable to those of the alternative search-based solutions at a fraction of the computational cost.