Abstract: A system for polarization imaging comprises an optical diffuser characterized by a point spread function (PSF), an image sensor, a spatially multiplexed polarization filter between the optical diffuser and the image sensor, and an image processor. The image processor receives signals from the image sensor and reconstructs, based on the PSF, a separate image for each polarization direction formed on the polarization filter.

Abstract: A system for depth estimation, comprises at least a first and a second depth estimation optical systems, each configured for receiving a light beam from a scene and estimating depths within the scene, wherein the first system is a monocular depth estimation optical system; and an image processor, configured for receiving depth information from the first and second systems, and generating a depth map or a three-dimensional image of the scene based on the received depth information.

Abstract: A device for authentication of a three-dimensional object includes an imaging array having a sensor configured to generate first and second sparse views of a surface of the three-dimensional object that faces the imaging array, and a processing circuitry. The processing circuitry is configured to: interpolate the first and second sparse views to obtain first and second interpolated images; calculate a planar disparity function for a plurality of image pixels of one of the first or second interpolated images; generate a projected image by displacing the plurality of image pixels of one of the first or the second interpolated images using the planar disparity function; and compare the projected image with the other of the first or second interpolated images to determine conformance of the planar disparity function with the interpolated images of the surface of the object.

Abstract: Presented herein are methods and systems for training a model, specifically a machine learning model, for example, a Deep Neural Network (DNN) for signal reconstruction in an iterative process comprising a plurality of training iterations and use of the trained DNN thereof. Each of the iterations comprises receiving a record associating a compressed signal created according to a sensing matrix selected from a plurality of sensing matrixes with a respective signal originated from a signal source and used for compressing the at least one compressed signal according to the selected sensing matrix, feeding the record and the sensing matrix to train a model and outputting the trained model which may be used for reconstructing one or more new signals originated from the signal source. Wherein at least two of the plurality of sensing matrixes are fed during at least two separate iterations of the plurality of training iterations.

Abstract: A method of processing an input image comprises receiving the input image, storing the image in a memory, and accessing, by an image processor, a computer readable medium storing a trained deep learning network. A first part of the deep learning network has convolutional layers providing low-level features extracted from the input image, and convolutional layers providing a residual image. A second part of the deep learning network has convolutional layers for receiving the low-level features and extracting high-level features based on the low-level features. The method feeds the input image to the trained deep learning network, and applies a transformation to the residual image based on the extracted high-level features.

Abstract: A method of processing an input image comprises receiving the input image, storing the image in a memory, and accessing, by an image processor, a computer readable medium storing a trained deep learning network. A first part of the deep learning network has convolutional layers providing low-level features extracted from the input image, and convolutional layers providing a residual image. A second part of the deep learning network has convolutional layers for receiving the low-level features and extracting high-level features based on the low-level features. The method feeds the input image to the trained deep learning network, and applies a transformation to the residual image based on the extracted high-level features.

Abstract: A method of designing an element for the manipulation of waves, comprises: accessing a computer readable medium storing a machine learning procedure, having a plurality of learnable weight parameters. A first plurality of the weight parameters corresponds to the element, and a second plurality of the weight parameters correspond to an image processing. The method comprises accessing a computer readable medium storing training imaging data, and training the machine learning procedure on the training imaging data, so as to obtain values for at least the first plurality of the weight parameters.

Abstract: A method of processing an input image comprises receiving the input image, storing the image in a memory, and accessing, by an image processor, a computer readable medium storing a trained deep learning network. A first part of the deep learning network has convolutional layers providing low-level features extracted from the input image, and convolutional layers providing a residual image. A second part of the deep learning network has convolutional layers for receiving the low-level features and extracting high-level features based on the low-level features. The method feeds the input image to the trained deep learning network, and applies a transformation to the residual image based on the extracted high-level features.