A Lensless Polarization Camera

Abstract

Polarization imaging is a technique that creates a pixel map of the polarization state in a scene. Although invisible to the human eye, polarization can assist various sensing and computer vision tasks. Existing polarization cameras use spatial or temporal multiplexing, which increases the camera volume, weight, cost, or all of the above. Recent lensless imaging approaches, such as DiffuserCam, have demonstrated that compact imaging systems can be realized by replacing the lens with a coding element and performing computational reconstruction. In this work, we propose a compact lensless polarization camera composed of a diffuser and a simple striped polarization mask. By combining this optical design with a reconstruction algorithm that explicitly models the polarization-encoded lensless measurements, four linear polarization images are recovered from a single snapshot. Our results demonstrate the potential of lensless approaches for polarization imaging and reveal the physical factors that govern reconstruction quality, guiding the development of high-quality practical systems.

Figures (11)

• Figure 1: Overview of the proposed lensless polarization camera Given (a) a scene with polarized light, the imaging is performed using (b) a random diffuser. Polarization multiplexing is achieved using (c) a mask located on the sensor plane, where (e) demonstrates a single period of the polarization multiplexing pattern, encoding linear polarization orientations ($0^\circ,45^\circ,90^\circ$ and $135^\circ$). This imaging scheme results in (d) a diffused and multiplexed image. Using the diffuser PSF and polarization mask characteristics, the reconstruction algorithm recovers (f) polarization intensity images of the scene.
• Figure 2: Optical encoding elements. (a) Diffuser PSF. The random structure of the diffuser acts as multiple randomly distributed impulse-like features. This produces a spatially extended PSF with many narrow features that enable image reconstruction. (b) Polarization mask. Leveraging the ability to reconstruct images from partial sampling of the diffuser-encoded measurement, the mask is composed of linear polarizer stripes at the required orientations. The mask response is measured under back-illumination using a polarized light source, enabling the different transmissions to be clearly visualized. (c) A prototype lensless polarization camera integrating the diffuser and polarization mask at the sensor.
• Figure 3: Front illumination — matched-forward-model simulation. For each scene, the first row shows the lensless polarization reconstruction with the same polarization mask for data generation and reconstruction, and the second row shows the corresponding spatially aligned reference images acquired using a lens-based camera with an external rotating polarizer.
• Figure 4: Back illumination - matched-forward-model simulation:(top) lensless reconstruction using a measured polarization mask (matched setting),(middle) lensless reconstruction using an idealized polarization mask (matched setting), and (bottom) corresponding spatially aligned reference images acquired using a lens-based camera with an external rotating polarizer.
• Figure 5: Measured and perturbed polarization mask models used in the mismatch analysis. Top row: measured polarization mask responses corresponding to the four polarization orientations ($0^\circ,45^\circ,90^\circ,135^\circ$) obtained from the fabricated mask. Bottom row: examples of perturbed mask models derived from the measured $0^\circ$ mask, including additive Gaussian noise, Gaussian blurring, interpolation with an idealized simulated mask (mean), and an idealized simulated mask.