The general area of signal and imageprocessing that focuses upon the detection and identification of military targets is known as automatic target recognition. This paper compares the impact of alternative wavelet pr...
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ISBN:
(纸本)0819424846
The general area of signal and imageprocessing that focuses upon the detection and identification of military targets is known as automatic target recognition. This paper compares the impact of alternative waveletprocessing techniques upon the performance of neural networks being used for target detection. In particular, the use of a filter whose coefficients are a linear combination of wavelet coefficients gave rise to an energy distribution in which targets were more detectable with fewer false alarms than when the same targets were sought in images whose data dimensionality was reduced using a conventional wavelet.
Combined use of the X-ray (Radon) transform and the wavelet transform has proved to be useful in application areas such as diagnostic medicine and seismology. In the present paper, the wavelet X-ray transform is intro...
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ISBN:
(纸本)0819425915
Combined use of the X-ray (Radon) transform and the wavelet transform has proved to be useful in application areas such as diagnostic medicine and seismology. In the present paper, the wavelet X-ray transform is introduced. This transform performs one-dimensional wavelet transforms along lines in R-n, which are parameterized in the same fashion as for the X-ray transform. It is shown that the transform has the same convenient inversion properties as the wavelet transform. The reconstruction formula receives further attention in order to obtain usable discretizations of the transform. Finally, a connection between the wavelet X-ray transform and the filtered backprojection formula is discussed.
Integer based-matrix algorithms for discrete Haar transform (DHT) and discrete wavelet transform (DWT) are proposed with relation to the multiresolution representation (MRR). A recursive wavelet transform technique is...
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ISBN:
(纸本)0819425915
Integer based-matrix algorithms for discrete Haar transform (DHT) and discrete wavelet transform (DWT) are proposed with relation to the multiresolution representation (MRR). A recursive wavelet transform technique is used with a view to demonstrating simply lossy reconstructed images in contrast to an original image under the specified resolution size. A visual effect of reconstructed images with differnt appearance and image quality, caused by modifying or throwing away a part of the 2-D HT or WT coefficients, is discussed with a measure of quantitative evaluation such as similarity and/or modified similarity, and fidelity RMSE and/or PSNR.
A complete wavelet-based image storage and indexing system for progressive coding, indexing, retrieval. and transmission of images over the network is proposed in this research. New wavelet domain features which inclu...
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ISBN:
(纸本)0819425915
A complete wavelet-based image storage and indexing system for progressive coding, indexing, retrieval. and transmission of images over the network is proposed in this research. New wavelet domain features which include subband significance, decomposition structure. luminance and chrominance histograms, and the significance map of the lowest frequency channel are used to achieve content-based indexing and retrieval. The proposed indexing features take into account of the color: brightness, texture, frequency, and spatial information of a given query image. All features can be naturally extracted as a byproduct during the image compression stage with wavelets. Since coding and indexing are integrated in an unified framework in the proposed system, the database management is greatly simplified. Extensive experimental results are given to demonstrate the retrieval performance of the new approach.
wavelet transform has been widely used in many signal and imageprocessingapplications such as edge detection and data compression. For applications that tolerate a slight compromise in accuracy for faster speed, a f...
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ISBN:
(纸本)0819425915
wavelet transform has been widely used in many signal and imageprocessingapplications such as edge detection and data compression. For applications that tolerate a slight compromise in accuracy for faster speed, a fast approximation of wavelet transform is favorable. In this paper we propose a simple yet effective algorithm for fast wavelet transform. The use of fixed point numbers simplifies the hardware design and computation complexity than the use of a floating point arithmetic unit. Calculations are further reduced using our thresholding-in-calculations (TIC) technique to omit calculations of small terms that are negligible to the accumulated sum. The TIC technique basically determines whether a multiplication followed by an addition shall be executed using a look-up table with the quantized magnitude of multiplication operands as input parameters. Knowing the levels of quantization, any combination of the quantized multiplication operands can approximate the product and be compared to a predetermined threshold value. If the approximated product is greater than or equal to the threshold value, the corresponding entry in the look-up table is marked for multiplication;otherwise, no multiplication will be executed. Our simulation results show that our approximation algorithm is effective for wavelet transform of audio signals. In addition, when our algorithm is applied to a simple wavelet based edge detection algorithm, the detection result. is almost the same as the one using precise calculation of the wavelet transform.
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 ...
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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.
We present a comparative study between a complex wavelet Coefficient Shrinkage (WCS) filter and several standard speckle filters that are widely used in the radar imaging community (Lee, Kuan, Frost, Geometric, Kalman...
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ISBN:
(纸本)0819425915
We present a comparative study between a complex wavelet Coefficient Shrinkage (WCS) filter and several standard speckle filters that are widely used in the radar imaging community (Lee, Kuan, Frost, Geometric, Kalman, Gamma, etc.). The WCS filter is based on the use of Symmetric Daubechies (SD) wavelets which share the same properties as the real Daubechies wavelets but with an additional symmetry property. The filtering operation is an elliptical soft-thresholding procedure with respect to the principal axes of the 2-D complex wavelet coefficient distributions. Both qualitative and quantitative results (signal to mean square error ratio, equivalent number of looks, edgemap figure of merit) are reported. Tests have been performed using simulated speckle noise as well as real radar images. It is found that the WCS filter performs equally well as the standard filters for low-level noise and slightly outperforms them for higher-level noise.
This paper describes the current status of our program of work in the area of digital image enhancement using wavelet-based multi-scale processing. We are developing an all-waveletimageprocessing algorithm to enhanc...
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ISBN:
(纸本)085296692X
This paper describes the current status of our program of work in the area of digital image enhancement using wavelet-based multi-scale processing. We are developing an all-waveletimageprocessing algorithm to enhance the quality of direct digital thorax images, by the manipulation of the data within scale-specific sub-bands. This method avoids the presentation compromises which may result from the global application of unsharp mask based image enhancement methods which are commonly used in medical imaging. This is achieved by applying specific processing to image components according to their scale. In particular contrast enhancement, de-noising and sharpening stages are all tailored to the noise and feature characteristics of Thoravision digital chest X-ray images. Whilst our experiments to date convince us that processing digital X-ray images within the wavelet domain is a useful tool for improving diagnostic image quality, our concerns now focus on the subtlety of this processing, since it can be prone to artefact generation if applied incorrectly. We are also interested in optimising the way in which wavelet based image enhancement must be presented to our clinical colleagues to meet their diagnostic needs.
In this paper we give a brief introduction to filter banks over commutative rings. In contrast to filter banks over the real numbers, we employ finite ring arithmetic to control the number of bits in the signal repres...
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ISBN:
(纸本)0819425915
In this paper we give a brief introduction to filter banks over commutative rings. In contrast to filter banks over the real numbers, we employ finite ring arithmetic to control the number of bits in the signal representations. This way we avoid the coefficient swell problem that is preeminent in rings of characteristic zero. We derive decompositions for images that are tailored to dedicated hardware implementations. These decompositions reduce the size of line-buffers which dominate the silicon area in integrated circuit implementations. As an application, we derive a lossless compression scheme for 8 bit monochrome images using wavelet filters with values in the ring Z/256Z.
We present a regularized method for wavelet thresholding in a multiresolution framework. For astronomical applications, classical methods perform a standard thresholding by setting to zero non-significant coefficients...
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ISBN:
(纸本)0819425915
We present a regularized method for wavelet thresholding in a multiresolution framework. For astronomical applications, classical methods perform a standard thresholding by setting to zero non-significant coefficients. The regularized thresholding uses a Tikhonov regularization constraint to give a value for the non-significant coefficients. This regularized multiresolution thresholding;is used for various astronomical applications. In image filtering, the significant coefficients are kept, and we compute the new value for each non-significant coefficients according to the regularization constraint. In image compression, only the most significant wavelet coefficients are coded. With lossy compression algorithms such as hcompress, the compressed image has a block-like appearance because of coefficients that are set to zero over large areas. We apply the Tikhonov constraint to restore the coefficients lost during the compression. By this way the distortion is decreasing and the blocking: effect is removed. This regularization applies with any kind of wavelet functions. We compare the performances of the regularized and non-regularized compression algorithms for Haar and spline filters. We show that the point spread function can be used;Is an additional constraint in the restoration of astronomical objects with complex shape. We present a regularized decompression scheme that includes filtering, compression and image deconvolution in a multiresolution framework.
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