Optimal distribution of integration time for intensity measurements in degree of linear polarization polarimetry

Abstract

We consider the degree of linear polarization (DOLP) polarimetry system, which performs two intensity measurements at orthogonal polarization states to estimate DOLP. We show that if the total integration time of intensity measurements is fixed, the variance of the DOLP estimator depends on the distribution of integration time for two intensity measurements. Therefore, by optimizing the distribution of integration time, the variance of the DOLP estimator can be decreased. In this paper, we obtain the closed-form solution of the optimal distribution of integration time in an approximate way by employing Delta method and Lagrange multiplier method. According to the theoretical analyses and real-world experiments, it is shown that the variance of the DOLP estimator can be decreased for any value of DOLP. The method proposed in this paper can effectively decrease the measurement variance and thus statistically improve the measurement accuracy of the polarimetry system.

Polarimetric Imaging via Deep Learning: A Review

“…The inherent limitations of PI systems. One needs to invert the acquired multi-intensity images to obtain the polarization information [75,78,85]. This time-consuming process makes it challenging to handle changing scenes in real-time.…”

“…For example, in automatic driving tasks, adding polarization analysis into the optical imaging (OI) system can compensate drawbacks of conventional intensity-mode-based methods [71,72]. Yet, these essential polarization parameters are non-linearly deduced by polarized intensities and are pretty sensitive to noise [73][74][75][76][77]. This point can be found in Figure 1a, which shows an image of the angle of polarization (AoP) in a low-light condition.…”

“…By inverting these measurements, we can obtain the polarization information that can be used to characterize the beams or samples [7,[83][84][85][86]. This polarimetric information may be the Stokes vector S, the Mueller matrix M, the degree of polarization (DoP) P, or AoP θ [75,87,88]. Based on images of these polarization features, one can perform such applications as target detection [89][90][91][92][93], classification [12,[94][95][96][97], discrimination [98][99][100][101].…”

Data-driven polarimetric imaging: a review

“…Because the polarimetric parameters are always derived from the measured intensities through nonlinear operators, which would amplify the noise for the AoP, removing noise to precisely restore the polarization information is a significant task 107,119,120 . CNN have distinct advantages regarding extracting image features and hidden structures; thus, they are suitable for image restoration and enhancement in noisy environments.…”

Polarization Lidar: Principles and Applications

“…Obviously, P ∈ [0, 1], and the light is non-polarized when P = 0; completely polarized when P = 1; and partially polarized when P ∈ (0, 1) [81]. It should be noted that one also defines the DoLP by the following Equation ( 5) when there only two orthogonal polarization components (i.e., I and I ⊥ ) [81,82]. Another important parameter in the field of P-lidar is the depolarization ratio (i.e., R), which is also determined by I and I ⊥ [83,84].…”