Gaussian Scale Mixtures (GSMs) in overcomplete oriented pyramids are, arguably, one of the most powerful available tools for image denoising: 1) they provide a new mathematical frame for modelling the variance-adaptat...
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Gaussian Scale Mixtures (GSMs) in overcomplete oriented pyramids are, arguably, one of the most powerful available tools for image denoising: 1) they provide a new mathematical frame for modelling the variance-adaptation problem, an approach used in image denoising for the last 25 years;2) they are applicable to contaminating sources of any spectral density;3) they yield the smallest L2-norm distortion results in simulations under white Gaussian noise, up to this date;and 4) they allow for a solution, for the first time, to the problem of denoising images affected by unknown covariance noise. In this work, we focus first on the general properties of the GSMs. Then, we review the different ways GSMs have been used in overcomplete oriented pyramids (MAP-z-GSM, BLS-GSM, spatially variant GSM), and their applications: classical denoising, signal-dependent noise removal, unknown covariance noise removal and deblurring.
This paper introduces the Interlevel Product (ILP) which is a transform based upon the Dual-Tree Complex Wavelet. Coefficients of the ILP have complex values whose magnitudes indicate the amplitude of multilevel featu...
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ISBN:
(纸本)0780391349
This paper introduces the Interlevel Product (ILP) which is a transform based upon the Dual-Tree Complex Wavelet. Coefficients of the ILP have complex values whose magnitudes indicate the amplitude of multilevel features, and whose phases indicate the nature of these features (e.g. ridges vs. edges). In particular, the phases of ILP coefficients are approximately invariant to small shifts in the original images. We accordingly introduce this transform as a solution to coarse scale template matching, where alignment concerns between decimation of a target and decimation of a larger search image can be mitigated, and computational efficiency can be maintained. Furthermore, template matching with ILP coefficients can provide several intuitive "near-matches" that may be of interest in image retrieval applications.
Adaptive wavelets are important when the signals or environment are changing with time. Interferometric sensors (moire, Michelson, Mach-Zehnder, or shearing interferometers) are critical to many Smart Structures appli...
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ISBN:
(纸本)0819458031
Adaptive wavelets are important when the signals or environment are changing with time. Interferometric sensors (moire, Michelson, Mach-Zehnder, or shearing interferometers) are critical to many Smart Structures applications. Adaptive wavelets will be developed to achieve phase distortion correction. Phase distortion correction will be achieved by means of wavelet ridge extraction and phase conjugation.
Traditionally, Fourier transforms have been utilized for performing signal analysis and representation. But although it is straightforward to reconstruct a signal from its Fourier transform, no local description of th...
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Traditionally, Fourier transforms have been utilized for performing signal analysis and representation. But although it is straightforward to reconstruct a signal from its Fourier transform, no local description of the signal is included in its Fourier representation. To alleviate this problem, Windowed Fourier transforms and then Wavelet transforms have been introduced, and it has been proven that wavelets give a better localization than traditional Fourier transforms, as well as a better division of the time- or space-frequency plane than Windowed Fourier transforms. Because of these properties and after the development of several fast algorithms for computing the wavelet representation of any signal, in particular the Multi-Resolution Analysis (MRA) developed by Mallat, wavelet transforms have increasingly been applied to signal analysis problems, especially real-life problems, in which speed is critical. In this article we present and compare efficient wavelet decomposition algorithms on different parallel architectures. We report and analyze experimental measurements, using NASA remotely sensed images. Results show that our algorithms achieve significant performance gains on current high-performance parallel systems, and meet scientific applications and multimedia requirements. The extensive performance measurements collected over a number of high-performance computer systems have revealed important architectural characteristics of these systems, in relation to the processing demands of the wavelet decomposition of digital images.
The authors present a statistical approach to speckle reduction in medical ultrasound B-scan images based on maximum a posteriori (MAP) estimation in the wavelet domain. In this framework, a new class of statistical m...
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The authors present a statistical approach to speckle reduction in medical ultrasound B-scan images based on maximum a posteriori (MAP) estimation in the wavelet domain. In this framework, a new class of statistical model for speckle noise is proposed to obtain a simple and tractable solution in a closed analytical form. The proposed method uses the Rayleigh distribution for speckle noise and a Gaussian distribution for modelling the statistics of wavelet coefficients in a logarithmically transformed ultrasound image. The method combines the MAP estimation with the assumption that speckle is spatially correlated within a small window and designs a locally adaptive Bayesian processor whose parameters are computed from the neighboring coefficients. Further, the locally adaptive estimator is extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. The experimental results show that the proposed method clearly outperforms the state-of-the-art medical image denoising algorithm of Pizurica et al., spatially adaptive single-resolution methods and band-adaptive multi-scale soft-thresholding techniques in terms of quantitative performance as well as in terms of visual quality of the images. The main advantage of the new method over the existing techniques is that it suppresses speckle noise well, while retaining the structure of the image, particularly the thin bright streaks, which tend to occur along boundaries between tissue layers.
The field of superresolution has seen a tremendous growth in interest over the past decade. High resolution images are crucial in several applications including medical imaging and diagnosis, military surveillance, sa...
The field of superresolution has seen a tremendous growth in interest over the past decade. High resolution images are crucial in several applications including medical imaging and diagnosis, military surveillance, satellite and astronomical imaging, and remote sensing. Constraints due to factors such as technology, cost, size, weight, and quality prevent the use of sensors with the desired resolution in image capture devices and consequently, necessitate the design of superresolution algorithms to achieve the desired image resolution. wavelets have emerged as a powerful tool in signalprocessing and many other fields. Second generation wavelets were recently introduced and their flexibility and versatility has resulted in an ever-growing number of applications. They have been used in fields ranging from the solution of partial differential equations to mesh refinement and mod- eling in computer graphics. Two prominent properties of second generation wavelets, viz. the ability to handle irregular sampling structures and the adaptation to arbitrary boundaries, which are at the heart of image sequence superresolution, motivated this research on superresolution algorithms based on second generation wavelets. The devel- oped techniques also achieve simultaneous noise filtering by thresholding the computed wavelet coefficients prior to reconstruction. Analysis leading to the selection of a thresh- old that yields an optimal trade-off between noise reduction and blur introduction due iii to thresholding is subsequently presented. Since the choice of prediction neighborhood in second generation wavelet transforms influences reconstructed image quality, an adap- tive neighborhood based on approximated gradients is proposed to enhance the quality of edges in the reconstructed images. Simulation results that demonstrate the superior performance of the developed techniques are also included. In addition to noise, blur commonly affects the quality of the captured images/video.
Smart pixels permit rapid signalprocessing through the use of integrated photodetectors and processing electronics on a single semiconductor chip. Smart pixels with smart illumination can increase the dynamic range a...
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Smart pixels permit rapid signalprocessing through the use of integrated photodetectors and processing electronics on a single semiconductor chip. Smart pixels with smart illumination can increase the dynamic range and functionality of smart pixels by employing optoelectronic feedback to control the illumination of a scene. This combination of smart pixels and optoelectronic feedback leads to many potential sensor applications, including normalized differential detection, which is modeled and demonstrated here. (c) 2005 Optical Society of America.
Wavelet difference reduction (WDR) has recently been proposed as a method for efficient embedded image coding. In this paper, the WDR algorithm is analysed and four new techniques are proposed to either reduce its com...
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Wavelet difference reduction (WDR) has recently been proposed as a method for efficient embedded image coding. In this paper, the WDR algorithm is analysed and four new techniques are proposed to either reduce its complexity or improve its rate distortion (RD) performance. The first technique, dubbed modified WDR-A (MWDR-A), focuses on improving the efficiency of the arithmetic coding (AC) stage of the WDR. Based on experiments with the statistics of the output symbol sequence, it is shown that the symbols can either be arithmetic coded under different contexts or output without AC. In the second technique, MWDR-B, the AC stage is dropped from the coder. By employing MWDR-B, up to 20% of coding time can be saved without sacrificing the RD performance, when compared to WDR. The third technique focuses on the improvement of RD performance using context modelling. A low-complexity context model is proposed to exploit the statistical dependency among the wavelet coefficients. This technique is termed context-modelled WDR (CM-WDR), and acts without the AC stage to improve the RD performance by up to 1.5 dB over WDR on a set of test images, at various bit rates. The fourth technique combines CM-WDR with AC and achieves a 0.2 dB improvement over CM-WDR in terms of PSNR. The proposed techniques retain all the features of WDR, including low complexity, region-of-interest capability, and embeddedness.
This paper introduces a method by which intuitive feature entities can be created from ILP (InterLevel Product) coefficients. The ILP transform is a pyramid of decimated complex-valued coefficients at multiple scales,...
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ISBN:
(纸本)0780394038
This paper introduces a method by which intuitive feature entities can be created from ILP (InterLevel Product) coefficients. The ILP transform is a pyramid of decimated complex-valued coefficients at multiple scales, derived from dual-tree complexwavelets, whose phases indicate the presence of different feature types (edges and ridges). We use an Expectation-Maximization algorithm to cluster large ILP coefficients that are spatially adjacent and similar in phase. We then demonstrate the relationship that these clusters possess with respect to observable image content, and conclude with a took at potential applications of these clusters, such as rotation- and scale-invariant object recognition.
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