The discrete wavelet transform (DWT) gives a compact multiscale representation of signals and provides a hierarchical structure for signalprocessing. It has been assumed the DWT can fairly well decorrelate real-world...
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ISBN:
(纸本)0780362934
The discrete wavelet transform (DWT) gives a compact multiscale representation of signals and provides a hierarchical structure for signalprocessing. It has been assumed the DWT can fairly well decorrelate real-world signals. However a residual dependency structure still remains between wavelet coefficients. It. has been observed magnitudes of wavelet coefficients are highly correlated, both across the scale and at neighboring spatial locations. In this paper we present, a wavelet folding technique, which folds wavelet coefficients across the scale and removes the across-the-scale dependence to a larger extent. It produces an even more compact signal representation and the energy is more concentrated in a few large, coefficients. It has a great potential in applications such as image compression.
wavelet transform is an effective method for removal of noise from image. But traditional wavelet transform cannot improve the smooth effect and reserve image's precise details simultaneously;even false Gibbs phen...
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wavelet transform is an effective method for removal of noise from image. But traditional wavelet transform cannot improve the smooth effect and reserve image's precise details simultaneously;even false Gibbs phenomenon can be produced. This paper proposes a new image denoising method based on adaptive multiscale morphological edge detection beyond the above limitation. Firstly, the noisy image is decomposed by using one wavelet base. Then, the image edge is detected by using the adaptive multiscale morphological edge detection based on the wavelet decomposition. On this basis, wavelet coefficients belonging to the edge position are dealt with with the improved wavelet domain wiener filtering, and the others are dealt with with the improved Bayesian threshold and the improved threshold function. Finally, wavelet coefficients are inversely processed to obtain the denoised image. Experimental results show that this method can effectively remove the image noise without blurring edges and highlight the characteristics of image edge at the same time. The validation results of the denoised images with higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) demonstrate their robust capability for real applications in the future.
Over the past few years, wavelets have become extremely popular in signal and imageprocessingapplications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents whi...
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Over the past few years, wavelets have become extremely popular in signal and imageprocessingapplications. The classical linear wavelet transform, however, performs a homogeneous smoothing of the signal contents which, ill some cases, is not desirable. This has led to a growing interest in (nonlinear) wavelet representations that can preserve discontinuities, such as transitions and edges. In this paper, we present the construction of adaptive wavelets by means of all extension of the lifting scheme. The basic idea is to choose the update filters according to some decision criterion which depends on the local characteristics of the input signal. We show that these adaptive schemes yield lower entropies than schemes with fixed update filters, a property that is highly relevant in the context of compression. Moreover, we analyze the effect of a scalar uniform quantization and the stability in such adaptive wavelet decompositions. (c) 2005 Elsevier B.V. All rights reserved.
In highly computationally intensive fields such as signalprocessing, imageprocessing, computer graphics and visualization, much of the CPU time is spent in computing various transforms on the typically large data se...
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ISBN:
(纸本)0819429139
In highly computationally intensive fields such as signalprocessing, imageprocessing, computer graphics and visualization, much of the CPU time is spent in computing various transforms on the typically large data sets which may also contain noise. Though extensive work has been reported on the de-noising and subsequent compression of the data, little of it has been reported on the de-noising and subsequent compression of the operators. Moreover, in the work reported so far, thresholding, which is essential for achieving denoising, has not been based on a specific criterion. In the present work, we propose modifications to this approach which result in significant savings in the computational cost of the associated transformations. We present wavelet based approaches to compress different operators. We present two methods to accomplish this. In the first method, we first apply a non-standard wavelet transform on the operator represented in its matrix form. This step is followed by an adaptive thresholding scheme of Donohoe and Johnstone(3), which results in the de-noised form of the operator. In the second method, the original matrix representation of the operator is split into two sparse diagonal dominant matrices, one in the spatial domain and the other in the wavelet domain. Although, there is a need to use the original signal in the spatial domain, the resulting decomposition actually requires only a portion of the operators. More importantly, the decomposition results in representations with very little total error. We find that the computational complexity of the transformation, using these methods, reduces to O(N) as opposed to O(N-2) (where N is the size of the data vector) observed with using the original, denser representation of the operator. In particular, Method 2 allows even more expedient processing of signals with greater accuracy. Hence, many transformations with operators can be represented in a diagonally dominant matrix form resulting in signifi
This paper presents a new method to extract, semi-automatically, quadrangular urban road network from high spatial resolution imagery. A quadrangular network is generally composed of different classes of streets in a ...
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ISBN:
(纸本)0819429139
This paper presents a new method to extract, semi-automatically, quadrangular urban road network from high spatial resolution imagery. A quadrangular network is generally composed of different classes of streets in a hierarchical system. The developed method is based both on the multiresolution analysis and on the wavelet transform. The multiresolution analysis allows a multiscale analysis of images and thus the extraction of the streets in a class-by-class way. The wavelet transform enables the modeling of information at different characteristic scales. In the problem, it allows the extraction of the topography of streets. These two mathematical tools are combined in the "a trous" algorithm. The application of this algorithm to images of urban areas has been used to develop semi-automatic multiresolution processing. This method will help photo-interpreters in their cartographic works by a partial automation of tasks.
wavelets are a recently developed mathematical tool for signal analysis. Informally, a wavelet is a short-term duration wave. wavelets are used as a kernel function in an integral transform, much in the same way that ...
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wavelets are a recently developed mathematical tool for signal analysis. Informally, a wavelet is a short-term duration wave. wavelets are used as a kernel function in an integral transform, much in the same way that sines and cosines are used in Fourier analysis or the Walsh functions in Walsh analysis. To date, the primary application of wavelets has been in the areas of signalprocessing, image compression, subband coding, medical imaging, data compression, seismic studies, denoising data, computer vision and sound synthesis. Here, the authors describe how wavelets may be used in the analysis of power system transients using computer implementation.
In this correspondence, a novel wavelet-based approach to recover continuous-tone (contone) images from halftone images is presented. wavelet decomposition of the halftone image facilitates a series of spatial and fre...
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In this correspondence, a novel wavelet-based approach to recover continuous-tone (contone) images from halftone images is presented. wavelet decomposition of the halftone image facilitates a series of spatial and frequency selective processing to preserve most of the original image contents while eliminating the halftone noise. Furthermore, optional nonlinear filtering can be applied as a postprocessing stage to create the final aesthetic contone image. This approach lends itself to practical applications since it is independent of parameter estimation and, hence, universal to all types of halftoned images, including those obtained by scanning printed halftones.
Optimal estimation of a two-dimensional (2-D) multichannel signal ideally decorrelates the data in both channel and space and weights the resulting coefficients according to their SNR. Many scenarios exist where the r...
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Optimal estimation of a two-dimensional (2-D) multichannel signal ideally decorrelates the data in both channel and space and weights the resulting coefficients according to their SNR. Many scenarios exist where the required second-order signal and noise statistics are not known in which the decorrelation is difficult or expensive to calculate. An asymptotically optimal estimation scheme proposed here uses a 2-D discrete wavelet transform to approximately decorrelate the signal in space and the discrete Fourier transform to decorrelate between channels. The coefficient weighting is replaced with a wavelet-domain thresholding operation to result in an efficient estimation scheme for both stationary and nonstationary signals. in contrast to optimal estimation, this new scheme does not require second-order signal statistics, making it well suited to many applications. In addition to providing vastly improved visual quality, the new estimator typically yields signal-to-noise ratio gains 12 dB or higher for hyperspectral imagery and functional magnetic resonance images.
In this correspondence, we present a modification to the scanning approach in the set partitioning algorithm proposed by Said and Pearlman to exploit the correlation in a local neighborhood. The wavelet filters are ch...
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In this correspondence, we present a modification to the scanning approach in the set partitioning algorithm proposed by Said and Pearlman to exploit the correlation in a local neighborhood. The wavelet filters are characterized based on the wavelet coefficients obtained after the wavelet transform. Two new criteria are proposed for evaluating the performance of wavelets in lossless image compression applications: cumulative zerotree count and monotone spectral ordering of subbands produced after wavelet transform in a multiresolution scheme. Several wavelet filters are evaluated to test the evaluation criteria. The experimental results are presented to justify the proposed performance criteria.
A discrete wavelet transform (DWT) technique to calculate the spacing of moire pitches is proposed. The moire pattern image obtained with various resolutions are analyzed. By using the proposed DWT, the moire pattern ...
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A discrete wavelet transform (DWT) technique to calculate the spacing of moire pitches is proposed. The moire pattern image obtained with various resolutions are analyzed. By using the proposed DWT, the moire pattern image is transformed into a periodic waveform. This application indicates that the approach is very robust to noise and distortion.
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