Error model for linear DoFP imaging systems perturbed by spatially varying polarization states.

Division of focal plane (DoFP) polarization sensors can perform linear polarimetric imaging in one shot. However, since they use several neighboring pixels to estimate the polarization state, fast spatial variations of the scene may lead to estimation errors. We investigate the influence of the spatial variations of the three polarimetric parameters of interest (intensity, degree of linear polarization, and angle of polarization) on these errors. Using theoretical derivations and imaging experiments, we demonstrate that the spatial variations of intensity are the main source of estimation errors, much more than variations in the polarization state. Building on this analysis, we show that compensating the intensity variations within a superpixel is sufficient to reach the estimation performance of state-of-the-art demosaicing methods.

Definition of an error map for DoFP polarimetric images and its application to retardance calibration.

With the recent development of division of focal plane (DoFP) polarization sensors, it is possible to perform polarimetric analysis of a scene with a reduced number of acquisitions. One drawback of these sensors is that polarization estimation can be perturbed by the spatial variations of the scene. We thus propose a method to compute a map that indicates where polarization estimation can be trusted in the image. It is based on two criteria: the consistency between the intensity measurements inside a super-pixel and the detection of spatial intensity variations. We design both criteria so that a constant false alarm rate can be set. We demonstrate the benefit of this method to improve the precision of dynamic retardance calibration of DoFP-based full Stokes imaging systems.

When is retardance autocalibration of microgrid-based full Stokes imagers possible and useful?

Full Stokes polarimetric images can be obtained from two acquisitions with a microgrid polarization camera equipped with a retarder. When the retardance is imperfectly known, it can be calibrated from the measurements, but this requires three image acquisitions and may cause divergence of estimation variance at a low signal-to-noise ratio. We determine closed-form equations allowing one to decide in which experimental conditions autocalibration is possible and useful, and to quantify the performance gain obtained in practice. These results are validated by real-world experiments.

Theory of autocalibration feasibility and precision in full Stokes polarization imagers.

We propose a general theory of simultaneous estimation of Stokes vector and instrumental autocalibration of polarization imagers. This theory is applicable to any polarization imager defined by its measurement matrix. We illustrate it on the example of retardance autocalibration in a large class of polarization imagers based on rotating retarders and polarimeters. We show that although all these architectures can yield optimal estimation precision of the Stokes vector if they are properly configured, they do not have the same autocalibration capacity and have to be specifically optimized for that purpose. These results are important to determine the best compromise between autocalibration capacity and polarimetric precision in practical applications.

Image reconstruction and enhancement by deconvolution in scatter-plate microscopy.

We investigated the capabilities of deconvolution for image enhancement in scatter-plate microscopy. This lensless imaging technique enables the investigation of microstructures through scattering media by cross-correlating the scattered light intensity with a previously recorded point spread function (PSF) of the scattering medium. The autocorrelation function of the PSF appears as the transfer function of the imaging process. Deconvolution methods use the knowledge of this transfer function to enhance the image quality by reducing the blur and strengthening the contrast with the objective to achieve diffraction-limited resolution. We obtained significant image enhancement both with means of inverse filtering and by applying iterative deconvolution algorithms.