This paper introduces a single-image super-resolution approach which is based on sparse representation over dictionaries learned in the wavelet domain. The diagonal detail subband learning and reconstruction is improv...
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ISBN:
(纸本)9781467355636;9781467355629
This paper introduces a single-image super-resolution approach which is based on sparse representation over dictionaries learned in the wavelet domain. The diagonal detail subband learning and reconstruction is improved by designing two diagonal dictionaries;one for the diagonal and another for the anti-diagonal orientations. Four pairs (low resolution and high resolution) of subband dictionaries are designed. The sparse representation coefficients for the respective low and high resolution images are assumed to be the same. The proposed algorithm is compared with the leading super-resolution techniques and is shown to excel both visually and quantitatively, with an average PSNR raise of 0.82 dB over the Kodak set. Moreover, this algorithm is shown to significantly reduce the dictionary learning computational complexity by designing compactly sized structural dictionaries.
We introduce an isotropic measure of local contrast for natural images that is based on analytic filters and present the design of directional wavelet frames suitable for its computation. We show how this contrast mea...
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ISBN:
(纸本)0819437646
We introduce an isotropic measure of local contrast for natural images that is based on analytic filters and present the design of directional wavelet frames suitable for its computation. We show how this contrast measure can be used within a masking model to facilitate the insertion of a watermark in an image while minimizing visual distortion.
A technique is presented to minimise false decisions in automotive radars operating in close proximity. The technique also reduces the requirement on the power of the radar as signals can be detected with very low sig...
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ISBN:
(纸本)0780370414
A technique is presented to minimise false decisions in automotive radars operating in close proximity. The technique also reduces the requirement on the power of the radar as signals can be detected with very low signal to noise ratios. The signalprocessing is achieved in real time using a field programmable array.
Most digital signalprocessing methods have an underlying assumption of regularly-spaced data samples. However, many real-world data collection techniques generate data sets which are not sampled at evenly-spaced inte...
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ISBN:
(纸本)0819422134
Most digital signalprocessing methods have an underlying assumption of regularly-spaced data samples. However, many real-world data collection techniques generate data sets which are not sampled at evenly-spaced intervals, or which may have significant data dropout problems. Therefore, a method of interpolation is needed to model the signal on an even grid of arbitrary granularity. We propose the interpolation of nonuniformly sampled fields using a least- square fit of the data to a wavelet basis in a multiresolution setting.
We describe experiments that we have performed that address the issue of the relation between the same enunciations by different speakers. Our previous work indicated that frequencies are approximately scaled uniforml...
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ISBN:
(纸本)0819429139
We describe experiments that we have performed that address the issue of the relation between the same enunciations by different speakers. Our previous work indicated that frequencies are approximately scaled uniformly. In this paper we report results addressing possible corrections to uniform scaling. Our results show that the scaling is non uniform, that is the formant frequencies of different speakers scale differently at different frequencies. We discuss how this leads to the mathematical issue of separating the spectrum into a speaker dependent and speaker independent parts. We introduce the concept of a universal scaling function that is aimed at achieving this separation. The fundamental idea is to find a frequency axis transformation (warping function) which transforms the energy density spectrum (the squared absolute value of the Fourier transform of the enunciation) in such a way that a further Fourier transform of the resulting function achieves this separation. We discuss this procedure and relate it to the scale transform. Using real speech data we obtain such a transformation function. The resulting function is very similar to the Mel scale, which has been previously obtained only from psychoacoustic (hearing based) experiments. That similar scales are obtained from both hearing and speech production (as reported here) is fundamental to the understanding of speech and hearing.
14-bit or 16-bit pixel depth high dynamic-range images are acquired from visible band cameras and from infrared imaging devices which are more widely used nowadays. Usually, linear mapping is used to display these ima...
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ISBN:
(纸本)9781467355636;9781467355629
14-bit or 16-bit pixel depth high dynamic-range images are acquired from visible band cameras and from infrared imaging devices which are more widely used nowadays. Usually, linear mapping is used to display these images to operators. However, results of the researches done to map images into 0-255 range in recent years show that different techniques result in major differences at image perception and detail visibility. Successful compression of image dynamic range increases the operator awareness for surveillance systems and ensure more effective display of scene details to user. Besides, dynamic-range compression techniques effect the enhancement of the success rate of image target detection and tracking techniques. In this work, scene components are analyzed using wavelet coefficients and intensity distribution of scene components are extracted. Extracted intensity distribution is used to display scene components effectively.
In this paper, we propose an image restoration algorithm based on state-of-the-art wavelet domain statistical models. We present an efficient method to estimate the model parameters from the observations, and solve th...
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ISBN:
(纸本)0819441929
In this paper, we propose an image restoration algorithm based on state-of-the-art wavelet domain statistical models. We present an efficient method to estimate the model parameters from the observations, and solve the restoration problem in orthonormal and translation-invariant (TI) wavelet domains. Substantial improvements over previous wavelet-based restoration methods are obtained. The use of a TI wavelet transform further enhances the restoration performance. We study the improvement from the viewpoint of Bayesian estimation theory and show that replacing an estimator with its TI version will reduce the expected risk if the signal and the degradation model are stationary.
This paper investigates the relationship between the traditional wavelet (or matched filter) detector and the estimator correlator (EC) detector formulated in the wavelet domain. The EC detector is actually a weighted...
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ISBN:
(纸本)0819425915
This paper investigates the relationship between the traditional wavelet (or matched filter) detector and the estimator correlator (EC) detector formulated in the wavelet domain. The EC detector is actually a weighted wavelet detector, weighted by the scattering function that describes the medium and/or model. The wavelet detector is the optimum detector for point objects but it does not incorporate knowledge of the scattering environment. However, when imaging distributed objects, it is advantageous to take a priori information into account. The EC incorporates this information as a weight on the waveletimage and formulates an estimated spreading function which essentially achieves recombination of highlights and multipath energy. It can be shown that the EC reduces to the the wavelet detector when a point object is being imaged.
A system for selecting a single best view image chip from an IR video sequence and compression of the chip for transmission is presented. Moving object detection was done using the algorithm described in [1]. Eigenspa...
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ISBN:
(纸本)0780370414
A system for selecting a single best view image chip from an IR video sequence and compression of the chip for transmission is presented. Moving object detection was done using the algorithm described in [1]. Eigenspace classification has been implemented for best view selection. Fast algorithms for image chip compression have been developed in the wavelet domain by combining a non-iterative zerotree coding method with 2D-DPCM for both low and high frequency subbands and compared against existing schemes.
This work provides a new approach to estimate the parameters of a semi-parametric generalized linear model in the wavelet domain. The method is illustrated with the problem of detecting significant changes in fMRI sig...
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ISBN:
(纸本)0780370414
This work provides a new approach to estimate the parameters of a semi-parametric generalized linear model in the wavelet domain. The method is illustrated with the problem of detecting significant changes in fMRI signals that are correlated to a stimulus time course. The fMRI signal is described as the sum of two effects : a smooth trend and the response to the stimulus. The trend belongs to a subspace spanned by large scale wavelets. We have developed a scale space regression that permits to carry out the regression in the wavelet domain while omitting the scales that are contaminated by the trend. Experiments with fMRI data demonstrate that our approach can infer and remove drifts that cannot be adequately represented with low degree polynomials. Our approach results in a noticeable improvement by reducing the false positive rate and increasing the true positive rate.
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