DD-Net: spectral imaging from a monochromatic dispersed and diffused snapshot.

We propose a snapshot spectral imaging method for the visible spectral range using a single monochromatic camera equipped with a two-dimensional (2D) binary-encoded phase diffuser placed at the pupil of the imaging lens and by resorting to deep learning (DL) algorithms for signal reconstruction. While spectral imaging was shown to be feasible using two cameras equipped with a single, one-dimensional (1D) binary diffuser and compressed sensing (CS) algorithms [Appl. Opt.

Design of binary-phase diffusers for a compressed sensing snapshot spectral imaging system with two cameras.

We propose designs of pupil-domain optical diffusers for a snapshot spectral imaging system using binary-phase encoding. The suggested designs enable the creation of point-spread functions with defined optical response, having profiles that are dependent on incident wavefront wavelength. This efficient combination of dispersive and diffusive optical responses enables us to perform snapshot spectral imaging using compressed sensing algorithms while keeping a high optical throughput alongside a simple fabrication process.

Gaussian bandwidth selection for manifold learning and classification.

Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel's scale parameter, also referred to as the kernel's bandwidth, highly affects the performance of the task in hand.

Dual-camera snapshot spectral imaging with a pupil-domain optical diffuser and compressed sensing algorithms.

We propose a snapshot spectral imaging method for the visible spectral range using two digital cameras placed side-by-side: a regular red-green-blue (RGB) camera and a monochromatic camera equipped with a dispersive diffractive diffuser placed at the pupil of the imaging lens. While spectral imaging was shown to be feasible using a single monochromatic camera with a pupil diffuser [Appl. Opt.

Unsupervised tumor detection in Dynamic PET/CT imaging of the prostate.

Early detection and localization of prostate tumors pose a challenge to the medical community. Several imaging techniques, including PET, have shown some success. But no robust and accurate solution has yet been reached.

Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme.

Purpose: While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a machine learning scheme using basic and advanced MR sequences for distinguishing different types of brain tumors.

Methods: The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma).

Analysis of Human Brain Structure Reveals that the Brain "Types" Typical of Males Are Also Typical of Females, and Vice Versa.

Findings of average differences between females and males in the structure of specific brain regions are often interpreted as indicating that the typical male brain is different from the typical female brain. An alternative interpretation is that the brain types typical of females are also typical of males, and sex differences exist only in the frequency of rare brain types. Here we contrasted the two hypotheses by analyzing the structure of 2176 human brains using three analytical approaches.

Compressed sensing snapshot spectral imaging by a regular digital camera with an added optical diffuser.

We propose a spectral imaging method that allows a regular digital camera to be converted into a snapshot spectral imager by equipping the camera with a dispersive diffuser and with a compressed sensing-based algorithm for digital processing. Results of optical experiments are reported.

Accelerating Particle Filter Using Randomized Multiscale and Fast Multipole Type Methods.

Particle filter is a powerful tool for state tracking using non-linear observations. We present a multiscale based method that accelerates the tracking computation by particle filters. Unlike the conventional way, which calculates weights over all particles in each cycle of the algorithm, we sample a small subset from the source particles using matrix decomposition methods.

CT reconstruction from parallel and fan-beam projections by a 2-D discrete Radon transform.

The discrete Radon transform (DRT) was defined by Abervuch as an analog of the continuous Radon transform for discrete data. Both the DRT and its inverse are computable in O(n(2) log n) operations for images of size n × n. In this paper, we demonstrate the applicability of the inverse DRT for the reconstruction of a 2-D object from its continuous projections.

Accelerating x-ray data collection using pyramid beam ray casting geometries.

Image reconstruction from its projections is a necessity in many applications such as medical (CT), security, inspection, and others. This paper extends the 2-D Fan-beam method in [2] to 3-D. The algorithm, called Pyramid Beam (PB), is based upon the parallel reconstruction algorithm in [1].

Spectral multiplexing method for digital snapshot spectral imaging.

We propose a spectral imaging method for piecewise "macropixel" objects, which allows a regular digital camera to be converted into a digital snapshot spectral imager by equipping the camera with only a disperser and a demultiplexing algorithm. The method exploits a "multiplexed spectrum" intensity pattern, i.e.

Multilayered image representation: application to image compression.

The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. An image is parsed into a superposition of coherent layers: piecewise smooth regions layer, textures layer, etc.

Image coding with geometric wavelets.

This paper describes a new and efficient method for low bit-rate image coding which is based on recent development in the theory of multivariate nonlinear piecewise polynomial approximation. It combines a binary space partition scheme with geometric wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image. The GW method successfully competes with state-of-the-art wavelet methods such as the EZW, SPIHT, and EBCOT algorithms.

Edge-enhancement postprocessing using artificial dissipation.

We present a method to enhance, by postprocessing, the performance of gradient-based edge detectors. It improves the performance of the edge detector by adding terms which are similar to the artificial dissipation that appear in the numerical solution of hyperbolic PDEs. This term is added to the output of the edge detector.

Multisensor image registration via implicit similarity.

This paper presents an approach to the registration of significantly dissimilar images, acquired by sensors of different modalities. A robust matching criterion is derived by aligning the locations of gradient maxima. The alignment is achieved by iteratively maximizing the magnitudes of the intensity gradients of a set of pixels in one of the images, where the set is initialized by the gradient maxima locations of the second image.

The angular difference function and its application to image registration.

The estimation of large motions without prior knowledge is an important problem in image registration. In this paper, we present the angular difference function (ADF) and demonstrate its applicability to rotation estimation. The ADF of two functions is defined as the integral of their spectral difference along the radial direction.

Deblocking of block-transform compressed images using weighted sums of symmetrically aligned pixels.

A new class of related algorithms for deblocking block-transform compressed images and video sequences is proposed in this paper. The algorithms apply weighted sums on pixel quartets, which are symmetrically aligned with respect to block boundaries. The basic weights, which are aimed at very low bit-rate images, are obtained from a two-dimensional function which obeys predefined constraints.

A new family of spline-based biorthogonal wavelet transforms and their application to image compression.

In this paper. we design a new family of biorthogonal wavelet transforms and describe their applications to still image compression. The wavelet transforms are constructed from various types of interpolatory and quasiinterpolatory splines.

Pseudopolar-based estimation of large translations, rotations, and scalings in images.

One of the major challenges related to image registration is the estimation of large motions without prior knowledge. This paper presents a Fourier-based approach that estimates large translations, scalings, and rotations. The algorithm uses the pseudopolar (PP) Fourier transform to achieve substantial improved approximations of the polar and log-polar Fourier transforms of an image.

Fast motion estimation using bidirectional gradient methods.

Gradient-based motion estimation methods (GMs) are considered to be in the heart of state-of-the-art registration algorithms, being able to account for both pixel and subpixel registration and to handle various motion models (translation, rotation, affine, and projective). These methods estimate the motion between two images based on the local changes in the image intensities while assuming image smoothness. This paper offers two main contributions.