Browsing by Author "Guo, Haitao"

Now showing 1 - 14 of 14
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    Enhancement of Decompressed Images at Low Bit Rates
    (1994-07-20) Gopinath, Ramesh A.; Lang, Markus; Guo, Haitao; Odegard, Jan E.; Digital Signal Processing (http://dsp.rice.edu/)
    Transform coding at low bit rates introduces artifacts associated with the basis functions of the transform. For example, decompressed images based on the DCT (discrete cosine transform)- like JPEG16 - exhibit blocking artifacts at low bit rates. This paper proposes a post-processing scheme to enhance decompressed images that is potentially applicable in several situations. In particular, the method works remarkable well in "deblocking" of DCT compressed images. The method is non-linear, computationally efficient, and spatially adaptive - and has the distint feature that it removes artifacts while yet retaining sharp features in the images. An important implication of this result is that images coded using the JPEG standard can be efficiently post-processed to give significantly improved visual quality in the images.
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    Joint Compression and Speckle Reduction of SAR Images using Embedded Zerotree Models
    (1996-03-01) Odegard, Jan E.; Guo, Haitao; Burrus, C. Sidney; Baraniuk, Richard G.; Digital Signal Processing (http://dsp.rice.edu/)
    We propose a new method for speckle reduction in synthetic aperture radar (SAR) imagery based on the embedded zerotree image compression algorithm. This new approach to denoising is inspired by the realization that the wavelet transform domain and the zero-tree image model are natural for both compression and denoising. We illustrate the proposed scheme using fully polarimetric SAR images and a variety of compression ratios.
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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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    Nonlinear Wavelet Processing for Enhancement of Images
    (1994-05-20) Odegard, Jan E.; Lang, Markus; Guo, Haitao; Gopinath, Ramesh A.; Burrus, C. Sidney; Digital Signal Processing (http://dsp.rice.edu/)
    In this note we apply some recent results on nonlinear wavelet analysis to image processing. In particular we illustrate how the (soft) thresholding algorithm due to Donoho and Johnstone can successfully be used to remove speckle in SAR imagery. Furthermore, we also show that transform coding artifacts, such as blocking in the JPEG algorithm, can be removed to achieve a perceptually improved image by post-processing the decompressed image.
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    Simultaneous Speckle Reduction and Data Compression using Best Wavelet Packet Bases with Applications to SAR based ATD/R
    (1995-04-20) Wei, Dong; Guo, Haitao; Odegard, Jan E.; Lang, Markus; Burrus, C. Sidney; Digital Signal Processing (http://dsp.rice.edu/)
    We propose a novel method for simultaneous speckle reduction and data compression based on shrinking, quantizing and coding the wavelet packet coefficients of the logarithmically transformed image. A fast algorithm is used to find the best wavelet packet basis in the rate-distortion sense from the entire library of admissible wavelet packet bases. Soft-thresholding in wavelet domain can significantly suppress the speckles of the synthetic aperture radar (SAR) images while maintaining bright reflections for subsequent detection and recognition. Optimal bit allocation, quantization and entropy coding achieve the goal of compression while maintaining the fidelity of the SAR image.
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    Theory and applications of the shift-invariant, time-varying and undecimated wavelet transforms
    (1995) Guo, Haitao; Burrus, C. Sidney
    In this thesis, we generalize the classical discrete wavelet transform, and construct wavelet transforms that are shift-invariant, time-varying, undecimated, and signal dependent. The result is a set of powerful and efficient algorithms suitable for a wide variety of signal processing tasks, e.g., data compression, signal analysis, noise reduction, statistical estimation, and detection. These algorithms are comparable and often superior to traditional methods. In this sense, we put wavelets in action.
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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 Based Speckle Reduction with Applications to SAR based ATD/R
    (1994-11-20) Guo, Haitao; Odegard, Jan E.; Lang, Markus; Gopinath, Ramesh A.; Selesnick, Ivan W.; Burrus, C. Sidney; Digital Signal Processing (http://dsp.rice.edu/)
    This paper introduces a novel speckle reduction method based on thresholding the wavelet coefficients of the logarithmically transformed image. The method is computational efficient and can sinificantly reduce the speckle while preserving the resolution of the original image. Both soft and hard thresholding schemes are studied and the results are compared. When fully polarimetric SAR images are available, we proposed several approaches to combine the data from different polorizations to achieve even better performance. Wavelet processed imagery is shown to provide better detection performance for synthetic-aperture radar (SAR) based automatic target detection/recognition (ATD/R)problem.
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    Wavelet-Based Post-Processing of Low Bit Rate Transform Coded Images
    (1994-01-15) Gopinath, Ramesh A.; Lang, Markus; Guo, Haitao; Odegard, Jan E.; Digital Signal Processing (http://dsp.rice.edu/); CML (http://cml.rice.edu/)
    In this paper we propose a novel method based on wavelet thresholding for enhancement of decompressed transform coded images. Transform coding at low bit rates typically introduces artifacts associated witht he basis functions of the transform. In particular, the method works remarkably well in "deblocking" of DCT compressed images. The method is nonlinear, computationally efficient, and spatially adaptive and has the distinct feature that it removes artifacts yet retain sharp features in the images. An important implication of this result is that iamges coded using the JPEG standard can efficiently be postprocessed to give significantly improved visual quality in the images. The algorithm can use a conventional JPEG encoder and decoder for which VLSI chips are available.
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    Wavelet-Based Post-Processing of Low Bit Rate Transform Coded Images
    (1994-11-01) Gopinath, Ramesh A.; Lang, Markus; Guo, Haitao; Odegard, Jan E.; Digital Signal Processing (http://dsp.rice.edu/)
    In this paper we propose a novel method based on wavelet thresholding for enhancement of decompressed transform coded images. Transform coding at low bit rates typically introduces artifacts associated witht he basis functions of the transform. In particular, the method works remarkably well in "deblocking" of DCT compressed images. The method is nonlinear, computationally efficient, and spatially adaptive and has the distinct feature that it removes artifacts yet retain sharp features in the images. An important implication of this result is that iamges coded using the JPEG standard can efficiently be postprocessed to give significantly improved visual quality in the images. The algorithm can use a conventional JPEG encoder and decoder for which VLSI chips are available.
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    Wavelets for approximate Fourier transform and data compression
    (1997) Guo, Haitao; Burrus, C. Sidney
    This dissertation has two parts. In the first part, we develop a wavelet-based fast approximate Fourier transform algorithm. The second part is devoted to the developments of several wavelet-based data compression techniques for image and seismic data. We propose an algorithm that uses the discrete wavelet transform (DWT) as a tool to compute the discrete Fourier transform (DFT). The classical Cooley-Tukey FFT is shown to be a special case of the proposed algorithm when the wavelets in use are trivial. The main advantage of our algorithm is that the good time and frequency localization of wavelets can be exploited to approximate the Fourier transform for many classes of signals, resulting in much less computation. Thus the new algorithm provides an efficient complexity versus accuracy tradeoff. When approximations are allowed, under certain sparsity conditions, the algorithm can achieve linear complexity, i.e. O(N). The proposed algorithm also has built-in noise reduction capability. For waveform and image compression, we propose a novel scheme using the recently developed Burrows-Wheeler transform (BWT). We show that the discrete wavelet transform (DWT) should be used before the Burrows-Wheeler transform to improve the compression performance for many natural signals and images. We demonstrate that the simple concatenation of the DWT and BWT coding performs comparably as the embedded zerotree wavelet (EZW) compression for images. Various techniques that significantly improve the performance of our compression scheme are also discussed. The phase information is crucial for seismic data processing. However, traditional compression schemes do not pay special attention to preserving the phase of the seismic data, resulting in the loss of critical information. We propose a lossy compression method that preserves the phase as much as possible. The method is based on the self-adjusting wavelet transform that adapts to the locations of the significant signal components. The elegant method of embedded zerotree wavelet compression is modified and incorporated into our compression scheme. Our method can be applied to both one dimensional seismic signals and two dimensional seismic images.