Technion Research & Development Foundation Limited

Nanometrics Inc.

Michael Elad

Amit Shachaf

Pedro Vagos

Yoav Schechner

Michal Aharon

Joseph Shtok

Alfred M Bruckstein

Michael Zibulevsky

Yuval Bahat

Dana Segev

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.

Deconvolution to reduce the effective spot size of a spectroscopic optical metrology device

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.

Example-based audio inpainting

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.

Example-based cross-modal denoising

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.

Image reconstruction in computed tomography

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.

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Kiriat Tivon, Israel

Michael Elad