Lifting based wavelet transform implementation not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. In this paper we first do a precision-analysi...
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ISBN:
(纸本)0780370414
Lifting based wavelet transform implementation not only helps in reducing the number of computations but also achieves lossy to lossless performance with finite precision. In this paper we first do a precision-analysis for the set of seven filters proposed by the JPEG2000 verification model. We determine the precision required to implement the filters using fixed point 2's complement arithmetic for lossless as well as lossy coding. Next we propose a unified architecture for implementing this set of filters for both the forward and the inverse transform.
The problem of image feature extraction for classification is difficult because of the high dimensionality inherent in image data. By extracting only relevant image features we reduce the dimensionality of the problem...
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ISBN:
(纸本)0819425915
The problem of image feature extraction for classification is difficult because of the high dimensionality inherent in image data. By extracting only relevant image features we reduce the dimensionality of the problem and improve classification accuracy. We further enhance classification performance by finding an optimal representation of the extracted image features which maximizes separability distance among classes. The principal tools used are Fourier series, wavelet packets, local discriminant basis analysis, and neural networks.
wavelet transform is a powerful and useful mathematical tool for signalprocessing. In this paper detailed description of procedures for numerical integration and derivation in Haar domain has been done. These procedu...
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ISBN:
(纸本)0819450804
wavelet transform is a powerful and useful mathematical tool for signalprocessing. In this paper detailed description of procedures for numerical integration and derivation in Haar domain has been done. These procedures are necessary both in control systems analysis, and, especially, for control design and development. A detailed comparison between classical methods of evaluation and the Haar way is presented and critically discussed.
Overcomplete wavelet representations have become increasingly popular for their ability to provide highly sparse and robust descriptions of natural signals. We describe a method for incorporating an overcomplete wavel...
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ISBN:
(纸本)0819450804
Overcomplete wavelet representations have become increasingly popular for their ability to provide highly sparse and robust descriptions of natural signals. We describe a method for incorporating an overcomplete wavelet representation as part of a statistical model of images which includes a sparse prior distribution over the wavelet coefficients. The wavelet basis functions are parameterized by a small set of 2-D functions. These functions are adapted to maximize the average log-likelihood of the model for a large database of natural images. When adapted to natural images, these functions become selective to different spatial orientations, and they achieve a superior degree of sparsity on natural images as compared with traditional wavelet bases. The learned basis is similar to the Steerable Pyramid basis, and yields slightly higher SNR for the same number of active coefficients. Inference with the learned model is demonstrated for applications such as denoising, with results that compare favorably with other methods.
Besov spaces classify signals and images through the Besov norm, which is based on a deterministic smoothness measurement. Recently, we revealed the relationship between the Besov norm and the likelihood of an indepen...
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ISBN:
(纸本)0819437646
Besov spaces classify signals and images through the Besov norm, which is based on a deterministic smoothness measurement. Recently, we revealed the relationship between the Besov norm and the likelihood of an independent generalized Gaussian wavelet probabilistic model. In this paper, we extend this result by providing an information-theoretic interpretation of the Besov norm as the Shannon codelength for signal compression under this probabilistic mode. This perspective unites several seemingly disparate signal/imageprocessing methods, including denoising by Besov norm regularization, complexity regularized denoising, minimum description length (MDL) processing, and maximum smoothness interpolation. By extending the wavelet probabilistic model (to a locally adapted Gaussian model), we broaden the notion of smoothness space to more closely characterize real-world data. The locally Gaussian model leads directly to a powerful wavelet-domain Wiener filtering algorithm for denoising.
In this paper the fusion of multimodal images into one greylevel image is aimed at. A multiresolution technique, based on the wavelet multiscale edge representation is applied. The fusion consists of retaining only th...
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ISBN:
(纸本)0819437646
In this paper the fusion of multimodal images into one greylevel image is aimed at. A multiresolution technique, based on the wavelet multiscale edge representation is applied. The fusion consists of retaining only the modulus maxima of the wavelet coefficients from the different bands and combining them. After reconstruction, a synthetic image is obtained that contains the edge information from all bands simultaneously. Noise reduction is applied by removing the noise-related modulus maxima. In several experiments on test images and multispectral satellite images, we demonstrate that the proposed technique outperforms mapping techniques, as PCA and SOM and other wavelet-based fusion techniques.
Data decorrelation and energy compaction are the two fundamental characteristics of wavelets that led to wavelet based image compression models. wavelet transform is not a perfect whitening transform;but it is viewed ...
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ISBN:
(纸本)0819450804
Data decorrelation and energy compaction are the two fundamental characteristics of wavelets that led to wavelet based image compression models. wavelet transform is not a perfect whitening transform;but it is viewed as an approximation to Karhunen-Loeve transform (KLT). In general, decorrelation does not imply statistical independence. Thus, a wavelet transform results in coefficients which exhibit inter and intra band dependencies. The energy compaction property of a wavelet is reflected in the coding performance, which can be measured by its coding gain. This paper investigates the above two important aspects of bi-orthogonal wavelets in the context of lossy compression. This investigation suggests that simple predictive models are sufficient to capture the dependencies exhibited by the wavelet coefficients. This paper also compares, the metrics that measure the performance of bi-orthogonal wavelets in lossy coding schemes.
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be pres...
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ISBN:
(纸本)081942840X
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be presented. The issues like multiresolution analysis in the context of sensor integration and pattern recognition and other salient features of the images using wavelets will be discussed in detail.
wavelet transform is an efficient way to perform multiresolution signal decomposition with a good time and frequency resolution and simple generation of families of functions to generate bases of the space of square s...
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ISBN:
(纸本)085296692X
wavelet transform is an efficient way to perform multiresolution signal decomposition with a good time and frequency resolution and simple generation of families of functions to generate bases of the space of square summable sequences. The basis functions used in the wavelet transform are all affine transformed versions of the original function. The compactly supported wavelets are defined from a scaling function that is the solution of a fractal-like equation. The multiresolution fractal coders lead to better results, both in terms of rate-distortion curves and visual quality.
Sparse signal representations and compressed sensing have found use in a large number of applications including image compression. Compressed sensing exploits the sparsity of naturally occurring images to reduce the v...
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ISBN:
(纸本)9781467321976;9781467321969
Sparse signal representations and compressed sensing have found use in a large number of applications including image compression. Compressed sensing exploits the sparsity of naturally occurring images to reduce the volume of the data by using less number of measurements. Inspired by this, we propose a new framework for image compression that combines compressed sensing theory with wavelet and vector quantization. wavelet transform is used to sparsify the input image while measurement vectors generated from the sparse vectors are transmitted using vector quantization. Simulation experiments are carried out to analyze the effects of various parameters on the image reconstruction quality. Results obtained have been found to be quite promising.
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