Abstract: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

Abstract: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

Abstract: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

Abstract: A method for packet loss concealment, that includes: continuously receiving a digital audio stream; extracting audio features from the digital audio stream while the digital audio stream is unharmed; and upon detecting a gap in the digital audio stream, filling the gap with one or more previous segments of the digital audio stream, wherein the filling is based on a matching of the one or more of the extracted audio features with one or more audio features adjacent to the gap.

Abstract: A method for cross-modal signal denoising, the method comprising using at least one hardware processor for: providing a first multi-modal signal comprising at least two relatively clear modalities; correlating features exhibited simultaneously in the at least two relatively clear modalities of the first multi-modal signal; providing a second multi-modal signal comprising at least one relatively noisy modality and at least one relatively clear modality; and denoising the at least one relatively noisy modality of the second multi-modal signal by associating between (a) features exhibited in the at least one relatively noisy modality of the second multi-modal signal and (b) the features of the first multi-modal signal.

Abstract: The effective spot size of a spectroscopic metrology device is reduced through deconvolution of a measurement spectra set acquired from a measurement target combined with a training spectra set obtained from a training target. The measurement spectra set may be obtained using sparse sampling of a grid scan of a measurement target. The training spectra set is obtained from a grid scan of a training target that is similar to the measurement target. The training spectra set and the measurement spectra set include spectra from different grid nodes. Deconvolution of the measurement spectra and the training spectra sets produces an estimated spectrum for the measurement target that is an estimate of a spectrum from the measurement target produced with incident light having an effective spot size that is smaller than the actual spot size. One or more characteristics of the measurement target may then be determined using the estimated spectrum.

Abstract: A method of projection domain processing based on a local transform and shrinkage for use in reconstructing digital images from a set of projections, the method including providing a target image of a target object, providing projection data of the target object, producing filtered projection data by applying a sparsifying transform and a shrinkage function to the projection data, followed by an inverse of the sparsifying transform, producing a restored image by applying a reconstruction transform to the filtered projection data, comparing the restored image to the target image, and producing an optimized projection domain shrinkage function by adapting the shrinkage function to minimize differences between the restored image and the target image. Related apparatus and methods are also described.

Abstract: A method for packet loss concealment, comprising: continuously receiving a digital audio stream; extracting audio features from the digital audio stream while the digital audio stream is unharmed; and upon detecting a gap in the digital audio stream, filling the gap with one or more previous segments of the digital audio stream, wherein said filling is based on a matching of the one or more of the extracted audio features with one or more audio features adjacent to the gap.

Abstract: A method for cross-modal signal denoising, the method comprising using at least one hardware processor for: providing a first multi-modal signal comprising at least two relatively clear modalities; correlating features exhibited simultaneously in the at least two relatively clear modalities of the first multi-modal signal; providing a second multi-modal signal comprising at least one relatively noisy modality and at least one relatively clear modality; and denoising the at least one relatively noisy modality of the second multi-modal signal by associating between (a) features exhibited in the at least one relatively noisy modality of the second multi-modal signal and (b) the features of the first multi-modal signal.

Abstract: A method of projection domain processing based on a local transform and shrinkage for use in reconstructing digital images from a set of projections, the method including providing a target image of a target object, providing projection data of the target object, producing filtered projection data by applying a sparsifying transform and a shrinkage function to the projection data, followed by an inverse of the sparsifying transform, producing a restored image by applying a reconstruction transform to the filtered projection data, comparing the restored image to the target image, and producing an optimized projection domain shrinkage function by adapting the shrinkage function to minimize differences between the restored image and the target image. Related apparatus and methods are also described.

Abstract: A signal processing system adapted for sparse representation of signals is provided, comprising: (i) one or more training signals; (ii) a dictionary containing signal-atoms; (iii) a representation of each training signal using a linear combination of said dictionary's signal-atoms; (iv) means for updating the representation of the training signal; (v) means for updating the dictionary one group of atoms at a time, wherein each atom update may include all representations referring to said updated atom; and (vi) means for iterating (iv) and (v) until a stopping rule is fulfilled. The system uses the K-SVD algorithm for designing dictionaries for sparse representation of signals.

Abstract: A signal processing system adapted for sparse representation of signals is provided, comprising: (i)one or more training signals; (ii) a dictionary containing signal-atoms; (iii) a representation of each training signal using a linear combination of said dictionary's signal-atoms; (iv) means for updating the representation of the training signal; (v) means for updating the dictionary one group of atoms at a time, wherein each atom update may include all representations referring to said updated atom; and (vi) means for iterating (iv) and (v) until a stopping rule is fulfilled. The system uses the K-SVD algorithm for designing dictionaries for sparse representation of signals.

Abstract: A signal processing system adapted for sparse representation of signals is provided, comprising: (i) one or more training signals; (ii) a dictionary containing signal-atoms; (iii) a representation of each training signal using a linear combination of said dictionary's signal-atoms; (iv) means for updating the representation of the training signal; (v) means for updating the dictionary one group of atoms at a time, wherein each atom update may include all representations referring to said updated atom; and (vi) means for iterating (iv) and (v) until a stopping rule is fulfilled. The system uses the K-SVD algorithm for designing dictionaries for sparse representation of signals.

Abstract: A method of creating a super-resolved color image from multiple lower-resolution color images is provided by combining a data fidelity penalty term, a spatial luminance penalty term, a spatial chrominance penalty term, and an inter-color dependencies penalty term to create an overall cost function. The data fidelity penalty term is an L1 norm penalty term to enforce similarities between raw data and a high-resolution image estimate, the spatial luminance penalty term is to encourage sharp edges in a luminance component to the high-resolution image, the spatial chrominance penalty term is to encourage smoothness in a chrominance component of the high-resolution image, and the inter-color dependencies penalty term is to encourage homogeneity of an edge location and orientation in different color bands. A steepest descent optimization is applied to the overall cost function for minimization by applying a derivative to each color band while the other color bands constant.

Abstract: The subject matter discloses a method and apparatus for re-sampling a sequence of images in order to improve its resolution, fill-in missing pixels, or de-interlace it. The method operates locally in the images to be processed, comparing pixel values of pixels surrounding the target pixel to pixel values of substantially the same locations in neighboring images. The comparison results in assigning a weight value for each area compared with the area containing the reviewed pixel. The pixel value of the reviewed pixel is updated as a function of multiplying pixel values of the areas by the weight assigned to each area. In another embodiment, areas within the same image are compared to areas containing the reviewed pixel. The subject matter also discloses two possible penalty functions for improving the resolution of images.

Abstract: A computer method of creating a super-resolved grayscale image from lower-resolution images using an L1 norm data fidelity penalty term to enforce similarities between low and a high-resolution image estimates is provided. A spatial penalty term encourages sharp edges in the high-resolution image, the data fidelity penalty term is applied to space invariant point spread function, translational, affine, projective and dense motion models including fusing the lower-resolution images, to estimate a blurred higher-resolution image and then a deblurred image. The data fidelity penalty term uses the L1 norm in a likelihood fidelity term for motion estimation errors. The spatial penalty term uses bilateral-TV regularization with an image having horizontal and vertical pixel-shift terms, and a scalar weight between 0 and 1. The penalty terms create an overall cost function having steepest descent optimization applied for minimization. Direct image operator effects replace matrices for speed and efficiency.

Abstract: An integrated method for both super-resolution and multi-frame demosaicing includes an image fusion followed by simultaneous deblurring and interpolation. For the case of color super-resolution, the first step involves application of recursive image fusion separately on the three different color layers. The second step is based on minimizing a maximum a posteriori (MAP) cost function. In one embodiment, the MAP cost function is composed of several terms: a data fidelity penalty term that penalizes dissimilarity between the raw data and the super-resolved estimate, a luminance penalty term that favors sharp edges in the luminance component of the image, a chrominance penalty term that favors low spatial frequency changes in the chrominance component of the image, and an orientation penalty term that favors similar edge orientations across the color channels. The method is also applicable to color super-resolution (without demosaicing), where the low-quality input images are already demosaiced.

Abstract: A signal processing system adapted for sparse representation of signals is provided, comprising: (i) one or more training signals; (ii) a dictionary containing signal-atoms; (iii) a representation of each training signal using a linear combination of said dictionary's signal-atoms; (iv) means for updating the representation of the training signal; (v) means for updating the dictionary one group of atoms at a time, wherein each atom update may include all representations referring to said updated atom; and (vi) means for iterating (iv) and (v) until a stopping rule is fulfilled. The system uses the K-SVD algorithm for designing dictionaries for sparse representation of signals.

Abstract: A method is provided of solving the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color superresolved images from low-quality monochromatic, color, or mosaiced frames. The invention includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking into account practical color measurements encountered in video sequences. For the case of translational motion and common space-invariant blur, the proposed invention is based on a very fast and memory efficient approximation of the Kalman filter (KF). Experimental results on both simulated and real data are supplied, demonstrating the invention algorithms, and their strength.

Abstract: An interactive system for obtaining data for diagnostic purposes from a server having access to stores of said data wherein the server supplies the data and software to a data requesting user that enables the user to receive the data progressively so as to decide during the receipt of the data what portions of the data are to be received thereby decreasing the time required to receive the data and that enables the user to load the software to process the supplied data.