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.