Browsing by Author "Wells, R.O."

Now showing 1 - 6 of 6
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    Noise Reduction Using an Undecimated Discrete Wavelet Transform
    (1995-01-15) Lang, Markus; Guo, Haitao; Odegard, Jan E.; Burrus, C. Sidney; Wells, R.O.; Digital Signal Processing (http://dsp.rice.edu/)
    A new nonlinear noise reduction method is presented that uses the discrete wavelet transform. Similar to Donoho and Johnstone, we employ thresholding in the wavelet transform domain but, following a suggestion by Coifman, we use an undecimated, shift-invariant, nonorthogonal wavelet transform instead of the usual orthogonal one. This new approach can be interpreted as a repeated application of the original Donoho and Johnstone method for different shifts. The main feature of the new algorithm is a significantly improved noise reduction compared to the original wavelet based approach, both the l2 error and visually, for a large class of signals. This is shown both theoretically as well as by experimental results.
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    Nonlinear Processing of a Shift Invariant DWT for Noise Reduction
    (1995-04-20) Lang, Markus; Guo, Haitao; Odegard, Jan E.; Burrus, C. Sidney; Wells, R.O.; Digital Signal Processing (http://dsp.rice.edu/); CML (http://cml.rice.edu/)
    A novel approach for noise reduction is presented. Similar to Donoho, we employ thresholding in some wavelet transform domain but use a nondecimated and consequently redundant wavelet transform instead of the usual orthogonal one. Another difference is the shift invariance as opposed to the traditional orthogonal wavelet transform. We show that this new approach can be interpreted as a repeated application of Donoho's original method. The main feature is, however, a dramatically improved noise reduction compared to Donoho's approach, both in terms of the l2 error and visually, for a large class of signals. This is shown by theoretical and experimental results, including synthetic aperture radar (SAR) images.
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    Nonlinear Processing of a Shift Invariant DWT for Noise Reduction
    (1995-03-20) Lang, Markus; Guo, Haitao; Odegard, Jan E.; Burrus, C. Sidney; Wells, R.O.; Digital Signal Processing (http://dsp.rice.edu/); CML (http://cml.rice.edu/)
    A novel approach for noise reduction is presented. Similar to Donoho, we employ thresholding in some wavelet transform domain but use a nondecimated and consequently redundant wavelet transform instead of the usual orthogonal one. Another difference is the shift invariance as opposed to the traditional orthogonal wavelet transform. We show that this new approach can be interpreted as a repeated application of Donoho's original method. The main feature is, however, a dramatically improved noise reduction compared to Donoho's approach, both in terms of the l2 error and visually, for a large class of signals. This is shown by theoretical and experimental results, including synthetic aperture radar (SAR) images.
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    Wavelet Based SAR Speckle Reduction and Image Compression
    (1995-01-15) Odegard, Jan E.; Guo, Haitao; Lang, Markus; Burrus, C. Sidney; Wells, R.O.; Novak, L.M.; Hiett, M.; Digital Signal Processing (http://dsp.rice.edu/)
    This paper evaluates the performance of the recently published wavelet based algorithm for speckle reduction of SAR images. The original algorithm, based on the theory of wavelet thresholding due to Donoho and Johnstone, has been shown to improve speckle statistics. In this paper we give more extensive results based on tests performed at Lincoln Laboratory (LL). The LL benchmarks show that the SAR imagery is significantly enhanced perceptually. Although the wavelet processed data results in an increase in the number of natural clutter false alarms (from trees etc.) an appropriately modified CFAR detector (i.e., by clamping the estimated clutter standard deviation) eliminates the extra false alarms. The paper also gives preliminary results on the performance of the new and improved wavelet denoising algorithm based on the shift invariant wavelet transform. By thresholding the shift invariant discrete wavelet transform we can further reduce speckle to achieve a perceptually superior SAR image with ground truth information significantly enhanced. Preliminary results on the speckle statistics of this new algorithm is improved over the classical wavelet denoising algorithm. Finally, we show that the classical denoising algorithm as proposed by Donoho and Johnstone and applied to SAR has the added benefit of achieving about 3:1 compression with essentially no loss in image fidelity.
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    Wavelet Based SAR Speckle Reduction and Image Compression
    (1995-04-01) Odegard, Jan E.; Guo, Haitao; Lang, Markus; Burrus, C. Sidney; Wells, R.O.; Novak, L.M.; Hiett, M.; Digital Signal Processing (http://dsp.rice.edu/); CML (http://cml.rice.edu/)
    This paper evaluates the performance of the recently published wavelet based algorithm for speckle reduction of SAR images. The original algorithm, based on the theory of wavelet thresholding due to Donoho and Johnstone, has been shown to improve speckle statistics. In this paper we give more extensive results based on tests performed at Lincoln Laboratory (LL). The LL benchmarks show that the SAR imagery is significantly enhanced perceptually. Although the wavelet processed data results in an increase in the number of natural clutter false alarms (from trees etc.) an appropriately modified CFAR detector (i.e., by clamping the estimated clutter standard deviation) eliminates the extra false alarms. The paper also gives preliminary results on the performance of the new and improved wavelet denoising algorithm based on the shift invariant wavelet transform. By thresholding the shift invariant discrete wavelet transform we can further reduce speckle to achieve a perceptually superior SAR image with ground truth information significantly enhanced. Preliminary results on the speckle statistics of this new algorithm is improved over the classical wavelet denoising algorithm. Finally, we show that the classical denoising algorithm as proposed by Donoho and Johnstone and applied to SAR has the added benefit of achieving about 3:1 compression with essentially no loss in image fidelity.
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    Wavelet Folding and Decorrelation Across the Scale
    (2000-06-01) Baraniuk, Richard G.; Wells, R.O.; Tian, Jun Feng; Digital Signal Processing (http://dsp.rice.edu/)
    The discrete wavelet transform (DWT) gives a compact multiscale representation of signals and provides a hierarchical structure for signal processing. 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.