Miguel Marquez

TL;DR: In this article, a Bayesian 3D reconstruction algorithm was proposed to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions, and the proposed model promoted spatial correlation between neighboring points within a given surface using spatial point processes.

TL;DR: This work proposes a compressive spectral light field imaging architecture that builds on the principles of the compressive imaging framework, to capture multiplexed representations of the multidimensional information, so that, less measurements are required to capture the SLF data cube.

TL;DR: This work introduces a novel and compact CSI architecture based on a deformable mirror and a colored-filter detector to produce compressive spatio-spectral projections without the need of a grating or prism and proposes a tensor-based reconstruction algorithm to recover the spatial-spectrals information from the compressed measurements.

TL;DR: This work proposes a Bayesian 3-D reconstruction algorithm that is able to find multiple surfaces per pixel, using few photons, i.e., shorter acquisitions, and yields better estimates than other existing methods for multi-surface reconstruction using multispectral Lidar data.

TL;DR: In this paper, a single image sensor is used to reconstruct the spectral and depth information of a scene from a limited set of two-dimensional projections, and a deformable mirror is used as a phase modulator to induce focal length sweeping while simultaneously introducing a controlled aberration.