We presents the interval interpolating wavelet transform for fast image compression. Comparing with the common used wavelet coding, this method is more systematically stable, with less computing complexity and the inh...
详细信息
ISBN:
(纸本)0819424935
We presents the interval interpolating wavelet transform for fast image compression. Comparing with the common used wavelet coding, this method is more systematically stable, with less computing complexity and the inherent parallel processing. In theory, it has the nearly optimal minimax compression characteristics. The simulation shows that the interval interpolating wavelet transform are more qualified and remove the artificial blocking effects (which is serious by DCT). The interval wavelets is introduced to deal with the boundary points of the finite localized image. This method do not only improve the compress rate, but also delete the quantization aliasing of the boundary pixels.
Many imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process. Unlike addi...
详细信息
ISBN:
(纸本)0819425915
Many imaging systems rely on photon detection as the basis of image formation. One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process. Unlike additive Gaussian noise, Poisson noise is signal-dependent, and consequently separating signal from noise is a very difficult task. In this paper, we develop a novel wavelet-domain filtering procedure for noise removal in photon imaging systems. The filter adapts to both the signal and the noise and balances the trade-off between noise removal and excessive smoothing of image details. Designed using the statistical method of cross-validation, the filter is simultaneously optimal in a small-sample predictive sum of squares sense and asymptotically optimal in the mean square error sense. The filtering procedure has a simple interpretation as a joint edge detection/estimation process. Moreover, we derive an efficient algorithm for performing the filtering that has the same order of complexity as the fast wavelet transform itself. The performance of the new filter is assessed with simulated data experiments and tested with actual nuclear medicine imagery.
Wavelet shrinkage is a signal estimation technique that exploits the remarkable abilities of the wavelet transform for signal compression. Wavelet shrinkage using thresholding is asymptotically optimal in a minimax me...
详细信息
ISBN:
(纸本)0819425915
Wavelet shrinkage is a signal estimation technique that exploits the remarkable abilities of the wavelet transform for signal compression. Wavelet shrinkage using thresholding is asymptotically optimal in a minimax mean-square error (MSE) sense over a variety of smoothness spaces. However, for any given signal, the MSE-optimal processing is achieved by the Wiener filter, which delivers substantially improved performance. In this paper, we develop a new algorithm for wavelet denoising that uses a wavelet shrinkage estimate as a means to design a wavelet-domain Wiener filter. The shrinkage estimate indirectly yields an estimate of the signal subspace that is leveraged into the design of the filter. A peculiar aspect of the algorithm is its use of two wavelet bases: one for the design of the empirical Wiener filter and one for its application. Simulation results show up to a factor of 2 improvement in MSE over wavelet shrinkage, with a corresponding improvement in visual quality of the estimate. Simulations also yield a remarkable observation: whereas shrinkage estimates typically improve performance by trading bias for variance or vice versa, the proposed scheme typically decreases both bias and variance compared to wavelet shrinkage.
We present a technique to compress scalar functions defined on 2-manifolds of arbitrary topology. Our approach combines discrete wavelet transforms with zerotree compression, building on ideas from three previous deve...
详细信息
ISBN:
(纸本)0819425869
We present a technique to compress scalar functions defined on 2-manifolds of arbitrary topology. Our approach combines discrete wavelet transforms with zerotree compression, building on ideas from three previous developments. the lifting scheme, spherical wavelets, and embedded zerotree coding methods. applications lie in the efficient storage and rapid transmission of complex data sets. Typical data sets are earth topography, satellite images, and surface parametrizations. Our contribution in this paper is the novel combination and application of these techniques to general 2-manifolds.
Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, Group-Normalized Wavelet Packet Transform (GNWPT), is...
详细信息
ISBN:
(纸本)0819424935
Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, Group-Normalized Wavelet Packet Transform (GNWPT), is presented for nonlinear signal filtering and extraction from the clutter or noise, together with the space(time)frequency masking technique. The extended l(p)-entropy improves the performance of GNWPT. For perception based image, soft-logic masking (different from Donoho's method) is emphasized to remove the aliasing with edge preserved. Lawton's method for complex valued wavelets construction is extended to generate the complex valued compactly supported wavelet packets for radar signal extraction. This kind of wavelet packets are symmetry and unitary orthogonal. Well-defined wavelet packets are chosen by the analysis remarks on their time-frequency characteristics. For real valued signalprocessing, such as images and ECG signal, the compactly supported spline or biorthogonal wavelet packets are preferred for perfect de-noising and filtering qualities.
The wavelet paradigm is now well established and has found many applications in signal and imageprocessing. Since also some of its precursors can be reformulated into wavelet terminology, it has become a preferred to...
详细信息
The wavelet paradigm is now well established and has found many applications in signal and imageprocessing. Since also some of its precursors can be reformulated into wavelet terminology, it has become a preferred tool for multiresolution analysis. We have given an overview of the application of wavelet multiresolution image analysis to texture. Results of recent studies prove the merits of the methods in practical segmentation and classification problems. Some aspects still need further investigation. Two were discussed: rotation invariance and colour texture.
The proceedings contain 56 papers. The topics discussed include: review of recent results on optimal orthonormal subband coders;comparison of wavelet image coding schemes for seismic data compression;image quality mea...
The proceedings contain 56 papers. The topics discussed include: review of recent results on optimal orthonormal subband coders;comparison of wavelet image coding schemes for seismic data compression;image quality measurement using the Haar wavelet;lossless image compression using wavelets over finite rings and related architectures;on consistent signal reconstruction from wavelet extrema representation;seismic imaging in wavelet domain: decomposition and compression of imaging operator;application of differential mapping and wavelet transform;usage of short wavelets for scalable audio coding;enhanced resolution control for video sequences;regularized multiresolution methods for astronomical image enhancement;weighted time-frequency and time-scale transforms for non-stationary signal detection;and a wavelet detector for distributed objects.
Severe weather such as tornadoes and large hail often emanates from thunderstorms that have persistent, well organized, rotating updrafts. These rotating updrafts, which are generally referred to as mesocyclones, appe...
详细信息
ISBN:
(纸本)0819425915
Severe weather such as tornadoes and large hail often emanates from thunderstorms that have persistent, well organized, rotating updrafts. These rotating updrafts, which are generally referred to as mesocyclones, appear as couplets of incoming and outgoing radial velocities to a single Doppler radar. Observations of mesocyclones reveal useful information on the kinematics in the vicinity of the storm updraft that, if properly interpreted, can be used to assess the likelihood and intensity of the severe weather. Automated algorithms for such assessments exist, but are inconsistent in their wind shear estimations and are prone to high false alarm rates. Reported here are the elements of a new approach that we believe will alleviate the shortcomings of previous mesocyclone detection algorithms. This wavelet-based approach enables us to focus on the known scales where mesocyclones reside. Common data quality problems associated with radar data such as noise and data gaps are handled effectively by the approach presented here. We demonstrate our approach with a one-dimensional test pattern, then with a two-dimensional synthetic mesocyclone vortex, and finally with a case study.
This paper presents the results of the development of an adaptive method for reducing signal-dependent noise, such as speckle noise, in a coherent imaging system signal, such as in medical ultrasound imaging. Speckle ...
详细信息
ISBN:
(纸本)0819425915
This paper presents the results of the development of an adaptive method for reducing signal-dependent noise, such as speckle noise, in a coherent imaging system signal, such as in medical ultrasound imaging. Speckle noise is filtered using nonlinear adaptive thresholding of received echo wavelet transform coefficients. Filtering speckle noise in ultrasound imaging enhances the resultant image by improving the signal-to-noise ratio. This method includes the steps of transforming the imaging system signal using discrete wavelet transformation to provide wavelet transform coefficients for each of the wavelet scales having different levels of resolution ranging from a finest wavelet scale to a coarsest wavelet scale;deleting the wavelet transform coefficients representing the finest wavelet scale;identifying, for each wavelet scale other than the finest wavelet scale, which of the wavelet transform coefficients are related to noise and which are related to a true signal through the use of adaptive non-linear thresholding;selecting those wavelet transform coefficients which are identified as being related to a true signal;and inverse transforming the selected wavelet transform coefficients using an inverse discrete wavelet transformation to provide an enhanced true signal with reduced noise. This method is shown to improve the signal-to-noise ratio by 2-5 dB in digital ultrasound images of real and phantom objects for a range of thresholding levels while preserving the contrast differences between regions and maintaining feature edges. The filtered images have an enhanced apparent contrast resulting from the reduction in the speckle noise and the preservation of the contrast differences.
Traditional objective metrics for the quality measure of coded images such as the mean squared error (MSE) and the peak signal-to-noise ratio (PSNR) do not correlate with the subjective human visual experiences well, ...
详细信息
ISBN:
(纸本)0819424358
Traditional objective metrics for the quality measure of coded images such as the mean squared error (MSE) and the peak signal-to-noise ratio (PSNR) do not correlate with the subjective human visual experiences well, since they do not take human perception into account. Quantification of artifacts resulted from lossy image compression techniques is studied based on a human visual system (HVS) model and the time-space localization property of the wavelet transform is exploited to simulate HVS in this research. As a result of our research, anew image quality measure by using the wavelet basis function is proposed. This new metric works for a wide variety of compression artifacts. Experimental results are given to demonstrate that it is more consistent with human subjective ranking.
暂无评论