We present a gonio-plenoptic imaging system that realizes a single-shot shape measurement for specular surfaces. The system is comprised of a collimated illumination source and a plenoptic camera. Unlike a conventional plenoptic camera, our system captures the BRDF variation of the object surface in a single image in addition to the light field information from the scene, which allows us to recover very fine 3D structures of the surface. The shape of the surface is reconstructed based on the reflectance property of the material rather than the parallax between different views. Since only a single-shot is required to reconstruct the whole surface, our system is able to capture dynamic surface deformation in a video mode. We also describe a novel calibration technique that maps the light field viewing directions from the object space to subpixels on the sensor plane. The proposed system is evaluated using a concave mirror with known curvature, and is compared to a parabolic mirror scanning system as well as a multi-illumination photometric stereo approach based on simulations and experiments.

Spectrally-coded plenoptic cameras have been developed for single-snapshot spectral imaging. Whereas reconstruction of spectral images from plenoptic sensor data has been studied in the past, rectification of those images according to a depth distribution in the scene has not been investigated. In this paper we describe a spectral parallax rectification algorithm for plenoptic cameras. The algorithm is designed to handle flexible filter partitions in the aperture and the small baselines inherent to plenoptic cameras. We demonstrate the algorithm's performance using checkerboard and captured scene data.

Optical polarization has shown promising applications in passive remote sensing. However, the combined effects of the scene characteristics, the sensor configurations, and the different processing algorithm implementations on the overall system performance have not been systematically studied. To better understand the effects of various system attributes and help optimize the design and use of polarimetric imaging systems, an analytical model has been developed to predict the system performance. The model propagates the first- and second-order statistics of radiance from a scene model to a sensor model and, finally, to a processing model. Validations with data collected from a division of time polarimeter are presented. Based on the analytical model, we then define a signal-to-noise ratio of the degree of linear polarization and receiver operating characteristic curves as two different system performance metrics to evaluate the polarimetic signatures of different objects, as well as the target detection performance. Several examples are presented to show the potential applications of the analytical model for system analysis.

Polarimetric imaging systems have shown promising applications in passive remote sensing. To better understand the effects of various system attributes and help optimize the de sign and use of polarimetric imaging systems, an analytical model is developed to predict the system performance. The model consists of scene, sensor, and processing system components. Some pre-processing procedures such as estimation of the Stokes vector and degree of linear polarization (DoLP) are currently considered in the processing model. Validation with data collected from a division of time polarimeter shows good agreement between model predictions and measurements. It has been shown that the analytical model is able to predict the general polarization behavior and data trends with different scene geometries.