Paul V. Rodriguez

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We develop new multiscale amplitude-modulation frequency-modulation (AM-FM) demodulation methods for image processing. The approach is based on three basic ideas: (i) AM-FM demodulation using a new multiscale filterbank, (ii) new, accurate methods for instantaneous frequency (IF) estimation, and (iii) multiscale least squares AM-FM reconstructions. In(More)
In this paper, we introduce new multiscale AM-FM demodulation algorithms that provide significant improvements in accuracy over previously reported approaches. The improvements are due to the use of new filterbanks based on separable filters supported in just two quadrants. The QEA, robust-QEA and Vakman methods are improved with this new filterbanks. A(More)
Multidimensional amplitude-modulation frequency-modulation (AM-FM) models allow us to describe continuous-scale modulations in digital images. AM-FM models have led to a wide range of applications ranging from image and video compression, video image segmentation, to image retrieval in digital libraries. We present new, two-dimensional algorithms that(More)
We studied neuroinflammation in individuals with late-life depression, as a risk factor for dementia, using [11C]PK11195 positron emission tomography (PET). Five older participants with major depression and 13 controls underwent PET and multimodal 3T magnetic resonance imaging (MRI), with blood taken to measure C-reactive protein (CRP). We found(More)
Spatially-varying signal content can be effectively modeled using amplitude modulation-frequency modulation (AM-FM) representations. The AM-FM representation allow us to extract instantaneous amplitude (IA) and instantaneous frequency (IF) components that can be used to measure non-stationary content in biomedical images and videos. This paper introduces a(More)
We present a first approach to a new method to compute the motion estimation in digital videos using the two-dimensional instantaneous frequency information computed using amplitude-modulation frequency-modulation (AM-FM) methods. The optical flow vectors are computed using an iteratively reweighted norm for total variation (IRN-TV) algorithm. We compare(More)
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