This paper describes the construction of a new multiresolutional decomposition with applications to image compression. The proposed method designs sparsity-distortion-optimized orthonormal transforms applied in wavele...
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ISBN:
(纸本)9781424479948
This paper describes the construction of a new multiresolutional decomposition with applications to image compression. The proposed method designs sparsity-distortion-optimized orthonormal transforms applied in wavelet domain to arrive at a multiresolutional representation which we term the Sparse Multiresolutional Transform (SMT). Our optimization operates over sub-bands of given orientation and exploits the inter-scale and intra-scale dependencies of wavelet coefficients over image singularities. The resulting SMT is substantially sparser than the wavelet transform and leads to compaction that can be exploited by well-known coefficient coders. Our construction deviates from the literature, which mainly focuses on model-based methods, by offering a data-driven optimization of wavelet representations. Simulation experiments show that the proposed method consistently offers better performance compared to the original wavelet-representation and can reach up to 1dB improvements within state-of-the-art coefficient coders.
in wavelets based coding applications, resolution scalability is achieved by retaining the low pass signal subband corresponds to the required resolution and discarding other high pass wavelet subbands. Aliasing is a ...
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ISBN:
(纸本)0780391349
in wavelets based coding applications, resolution scalability is achieved by retaining the low pass signal subband corresponds to the required resolution and discarding other high pass wavelet subbands. Aliasing is a common problem present in such downsampling. In this paper a novel technique for improving the low pass filter for improved downsampling is presented. This method uses an extra update step followed by P+U lifting scheme. The preprocessing update step is chosen as the dual update step associated with the wavelet. The spatially adaptive low pass (SALP) filtering concept is used for the second update step, leading to an overall low pass filter whose size adapts to the underlying signal content. The filter choices for the second update step is recovered at the decoder without any bookkeeping. Results using the 2D 5/3 wavelet with the extra pre-processing update step show improvements over conventional wavelets.
wavelet threshold algorithms replace wavelet coefficients with small magnitude by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local ...
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ISBN:
(纸本)0819432997
wavelet threshold algorithms replace wavelet coefficients with small magnitude by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. Although a wavelet transform has decorrelating properties, structures in images, like edges, are never decorrelated completely, and these structures appear in the wavelet coefficients. We therefore introduce a geometrical prior model for configurations of large wavelet coefficients and combine this with the local characterization da classical threshold procedure into a Bayesian framework. The threshold procedure selects the large coefficients in the actual image. This observed configuration enters the prior model, which, Pry itself, only describes configurations, not coefficient values. In this way, we can compute for each coefficient the probability of being "sufficiently clean".
In this work, we consider the problem of blind source separation in the wavelet domain via a Bayesian estimation framework. We use the sparsity and multiresolution properties of the wavelet coefficients to model their...
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ISBN:
(纸本)0819450804
In this work, we consider the problem of blind source separation in the wavelet domain via a Bayesian estimation framework. We use the sparsity and multiresolution properties of the wavelet coefficients to model their distribution by heavy tailed prior probability laws: the generalized exponential family and the Gaussian mixture family. Appropriate MCMC algorithms are developped in each case for the estimation purposes and simulation results are presented for comparison.
In this paper we aim at providing a robust and more compact approach for detecting edges compared to the traditional edge detection algorithms like Roberts, Sobel, Prewitt and evolutionary-inspired Ant Colony Optimiza...
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ISBN:
(纸本)9781467373869
In this paper we aim at providing a robust and more compact approach for detecting edges compared to the traditional edge detection algorithms like Roberts, Sobel, Prewitt and evolutionary-inspired Ant Colony Optimization (ACO) techniques. In this proposed approach, an ACO is used alongside Dual-Tree Complex wavelet Transform (DT-CWT) to detect and emphasize edges that would have been difficult to obtain with directly applying ACO or conventional edge detection algorithms. Initially the image is decomposed using DT-CWT to obtain the oriented wavelets and approximation versions of the original image. ACO is applied to each of the decomposed images and then image is reconstructed to get the processed image with the detected edges. The results obtained reveal superior, more detailed and emphasized edges than directly applying ACO or other conventional techniques. The proposed approach is also capable of identifying edges in slightly varying intensity regions.
wavelet domain image resolution enhancement algorithms assume that the available image is the low-frequency subband of a higher resolution image and high-frequency subbands are not available. Then, these high-frequenc...
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ISBN:
(纸本)9781424407194
wavelet domain image resolution enhancement algorithms assume that the available image is the low-frequency subband of a higher resolution image and high-frequency subbands are not available. Then, these high-frequency coefficients are estimated and the higher resolution image is generated by application of inverse wavelet transform. Some of these techniques have used probabilistic methods and utilisation of HMT (Hidden Markov Tree) was shown to produce promising results. HMT based methods model the wavelet coefficients as Gaussian distributions. However, as Gaussian distributions are symetrical around zero, coefficient signs are generated randomly and have an equal change of being positive or negative. In this paper, significance of having correst coefficient sign information is demonstrated and a post-processing method is proposed to increase the accuracy of the estimated signs.
We describe a method for learning an overcomplete set of basis functions for the purpose of modeling data with sparse structure. Such data are characterized by the fact that they require a relatively small number of n...
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ISBN:
(纸本)0819437646
We describe a method for learning an overcomplete set of basis functions for the purpose of modeling data with sparse structure. Such data are characterized by the fact that they require a relatively small number of non-zero coefficients on the basis functions to describe each data point. The sparsity of the basis function coefficients is modeled with a mixture-of-Gaussians distribution. One Gaussian captures non-active coefficients with a small-variance distribution centered at zero, while one or more other Gaussians capture active coefficients with a large-variance distribution. We show;that when the prior is in such a form, there exist efficient methods for learning the basis functions as well as the parameters of the prior. The performance of the algorithm is demonstrated on a number of test cases and also on natural images. The basis functions learned on natural images are similar to those obtained with other methods, but the sparse form of the coefficient distribution is much better described. Also, since the parameters of the prior are adapted to the data, no assumption about sparse structure in the images need be made a priori, rather it is learned from the data.
High-resolution optical mapping is an emerging technique to record the activation and propagation of transmembrane potential on the surface of cardiac tissues. Important electrodynamic information previously not avail...
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ISBN:
(纸本)0819429139
High-resolution optical mapping is an emerging technique to record the activation and propagation of transmembrane potential on the surface of cardiac tissues. Important electrodynamic information previously not available from extracellular electric recording could be extracted from these detailed optical recordings. The noise contamination in the images is a major obstacle that prohibits higher level of information extraction. Because the patterns of interest contain sharp wavefronts and structures that we wish to detect and track in a series of: flames, we seek to perform denoising based on wavelet decomposition approaches. Among the wavelet denoise methods that were tested in this preliminary study, the wavelet packet produced the best results that could be extended to denoise the entire image sequence for multi-dimensional information processing.
signal decomposition techniques are an important tool for analyzing nonstationary signals. The proper selection of time-frequency basis functions for the decomposition is essential to a variety of signalprocessing ap...
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ISBN:
(纸本)0819418447
signal decomposition techniques are an important tool for analyzing nonstationary signals. The proper selection of time-frequency basis functions for the decomposition is essential to a variety of signalprocessingapplications. The discrete wavelet transform (DWT) is increasingly being used for signal analysis, but not until recently has attention been paid to the time-frequency resolution property of wavelets. This paper describes additional results on our procedure to design wavelets with better time-frequency resolution. In particular, our optimal duration-bandwidth product wavelets (ODBW) have better duration-bandwidth product, as a function of wavelet-defining filter length N, than Daubechies' minimum phase and least- asymmetric wavelets, and Dorize and Villemoes' optimum wavelets over the range N equals 8 to 64. Some examples and comparisons with these traditional wavelets are presented.
In this paper we investigate the benefits of using local wavelet analysis to the face recognition problem. We examine two possible approaches to perform local wavelet analysis. In the first approach, discrete wavelet ...
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ISBN:
(纸本)9781424407194
In this paper we investigate the benefits of using local wavelet analysis to the face recognition problem. We examine two possible approaches to perform local wavelet analysis. In the first approach, discrete wavelet transform is performed on the entire face image and then the transformed image is partitioned into non-overlapping rectangular blocks. In the second approach, as in the JPEG2000 standard, the input face image is first partitioned into non-overlapping rectangular blocks, and then on each block discrete wavelet transformation is performed. Proposed approaches are tested against the occlusion problem using the AR face database and significant improvements are observed in the face recognition performance.
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