Data processing for sequential in time polarimeters based on the Data Reduction Matrix technique yield polarization artifacts in the presence of time varying signals. To overcome these artifacts, polarimeters are designed to operate at higher and higher speeds. In this paper we describe a band limited reconstruction algorithm that allows the measurement and processing of temporally varying Stokes parameters without artifacts. An example polarimeter consisting of a rotating retarder and polarizer is considered, and conventional processing methods are compared to a band limited reconstruction algorithm for the example polarimeter. We demonstrate that a significant reduction in error is possible using these methods.
©2011 Optical Society of America
Active and passive polarimeters are powerful tools for remote sensing and these have been developed in all regions of the optical spectrum from UV through the long wave IR . Imaging polarimeters have been used to detect targets in the presence of clutter , aid in target identification , penetrate scattering media [3–6] and aid in three-dimensional image reconstruction , among other tasks. Polarimeters have also been exploited for atmospheric sensing applications, including determination of aerosol properties , discrimination of ice/water phase particulates in clouds , and observation of plasmas in rocket engine exhaust . As the applications for polarimetry become increasingly diverse, one of the challenges of polarimetry is the understanding and mitigation of polarization artifacts that occur with scenes that vary rapidly with time and space.
The polarization information in a spatially incoherent optical field is typically described using the Stokes parameters Eq. (1) Ix, Iy, I 45, and I 135 are the fluxes observed through ideal linear polarizers at the indicated orientations, and IL and IR are fluxes through ideal left- and right-circular polarizers, respectively. Optical detectors cannot measure s 1, s 2, and s 3 directly. Instead, optical polarimeters modify the detected flux of the optical field in a polarization-dependent manner, and then reconstruct the Stokes parameters through an inversion process [1, 11].
Polarimeters are typically grouped into two classes . The first class – Division of Time (DoT) polarimeters – include those polarimeters that make modulated measurements sequentially in time using a single set of temporally varying polarization components and a single detector. The second class – referred to as simultaneous measurement polarimeters (SMP) – analyze the flux in several states simultaneously using strategies that include the use of multiple detectors [12, 13], spatial modulation [14–16], or spectral modulation . DoT polarimeters are widely considered to be more accurate because they are easier to calibrate and control, and tend to be the preferred choice for laboratory measurements where the Stokes parameters under measurement are not a function of time. However, DoT polarimeters are preferably not applied to rapidly time varying polarization signatures because of the false polarization artifacts which arise due to the time-varying signals that are introduced in the reconstruction process. Examples of DoT polarimeters applied to dynamic scenes have focused on employing extremely fast modulation schemes [18, 19].
The traditional categorization into DoT and SMP polarimeters is not necessarily the most useful way to classify polarimeters. An alternative distinction is between wavefront division polarimeters and modulated polarimeters. Wavefront division polarimeters split the light into multiple channels and make the constituent polarization measurements with independent hardware in each channel. Modulated polarimeters introduce a polarimetric modulation in time, space, wavelength, or some combination thereof, and measure the modulated signal using a single detector or detector array. The polarization signal is then determined by demodulating the information carried in the polarization-dependent side bands.
Wavefront division polarimeters have considerable hardware challenges associated with their construction. Multiple optical paths must be equalized, aligned, distortion matched, and cross-calibrated. Differential wavefront and polarimetric aberrations must be controlled from channel to channel. Temporal synchronization must be maintained. Ghost reflection and stray light variations from channel to channel introduce artifacts. When coherent light is used, speckle can present almost insurmountable problems. All of these factors raise issues that lead to increased cost and complexity in wavefront division polarimetry. The wavefront division advantage is that polarimetric imagery is produced at the full resolution of the individual detectors in a single simultaneous measurement cycle.
In contrast, a modulated polarimeter has several advantages over a wavefront division polarimeter working in the same domain. Alignment and synchronization are generally more easily obtained, though DoT polarimeters with moving optics suffer from beam wander artifacts that can degrade image quality . The penalty for using a modulated polarimeter is generally a loss of resolution in the dimension of the modulation, since a single detector is being used to measure multiple quantities. This loss of resolution is associated with polarimetric aliasing, especially when the signal bandwidth is not adequately considered.
In a recent paper, Tyo, et al. , considered the sub-class of SMP polarimeters known as Division of Focal Plane (DoFP), or microgrid, polarimeters. These DoFP polarimeters use a focal plane array matched with a pixelated micropolarizer array in order to modulate the flux spatially. DoFP devices have been popular, but can be limited because of the intrinsic spatial mis-registration of their polarization measurements and the resulting edge-enhancement artifacts . This limitation is analogous to the artifacts discussed above that occur when DoT polarimeters are used on time-varying scenes. Tyo, et al.. , derived a spatial band limit criterion on the input Stokes parameters that when satisfied guarantees artifact-free reconstruction of the Stokes parameters (in the absence of noise). DoT polarimeters are temporal analogues to microgrid polarimeters, so the spatial analysis of the DoFP polarimeters can be replicated in the time domain for division of time polarimeters. The implication of the band limit criteria is that the assumption that a Stokes signal is constant in time is unnecessarily limiting, just as the assumption for constant Stokes parameters across a 2x2 superpixel in a microgrid is unnecessarily limiting . There is a band limit criteria for DoT polarimeters for artifact free reconstruction (in noise free cases), and both the Stokes parameters and the reconstruction method must satisfy the band limit criteria.
The remainder of this manuscript is organized as follows: section 2 reviews the conventional polarimetric data reduction method. Section 3 describes modulated polarimeters using a systems formalism. Section 4 generalizes the pseudo inverse method of section 2 for an arbitrary window shape. Two sequential in time polarimeter configurations are evaluated in section 5. Section 6 contains the conclusions.
2. Polarimetric Data Reduction Matrix method for DoT Polarimeters
The conventional Polarimetric Data Reduction Matrix (PDRM) method for polarimeter data reduction is well known  and is only summarized here. The polarimeter performs a series of flux measurements by using a set of retarders and analyzers that are changed from measurement to measurement. A common example for DoT polarimeters is the rotation of a wave plate in front of a linear analyzer , but many other configurations are used [8, 23, 24]. The incident Stokes parameters are analyzed by the polarization optics, and the transmitted flux is measured by a detector. The measured flux as a function of time I(t) is given by the inner product11].
Among the set of matrix inversions, typically the pseudo inverse
The above discussion provides a linear algebra perspective to polarimeter operation. A frequency domain approach provides a different perspective, and an example is helpful here. Consider the simple case of the rotating analyzer linear polarimeter; an ideal linear analyzer rotates with a constant frequency f 0 (in units of rotations per second). The analyzer vector as a function of time isEq. (3) becomes Equation (8) indicates that the s 0 information is contained in the base band, (unmodulated DC) term, the s 1 information is contained in the in-phase (cosine) side band at 2 f 0, and the s 2 information is contained in the quadrature (sine) side band at 2 f 0. The polarization information could be demodulated using frequency-domain methods, which is often done for systems using photo elastic modulators operating at very high rates (∼10s of kHz) . However, the linear algebra formulation of Eq. (5) is often preferred for imaging polarimeters since it is easier to incorporate calibration, error analysis, and other practical considerations. In principle, only four measurements are required to uniquely invert Eq. (5) for the four Stokes parameters. However, it is common to employ more than four measurements in order to improve the accuracy of the polarimeter and reduce the effects of noise and systematic errors [8, 22, 25, 26].
3. A Linear Systems Formalism for Data Reduction
The polarimetry literature treats S as approximately constant in computing the PDRM with only a few exceptions [8, 27]. However, an analysis of Eq. (3) or Eq. (8) reveals that assuming constant Stokes parameters is overly limiting. Communications systems theory can be employed to allow the Stokes parameters to be varying in time, subject to a bandwidth criterion imposed by the reconstruction process. This result is analogous to results derived for spatially modulated imaging polarimeters [15, 16, 28] and spectrally modulated spectropolarimeters .
Assuming the Stokes parameters are functions of time and taking the Fourier transform of the signal I(t) in Eq. (2) yieldsEquation (9) is a deconvolution problem that can be inverted by careful design of the analyzer Stokes parameters A(t). For the example in Eq. (8) side bands in frequency space carry the polarization information as
Equation (10) demonstrates that requiring the Stokes parameters to be constant in time is an unnecessary restriction because perfect reconstruction can be achieved assuming that the Stokes parameters are band limited to frequencies below WB (as seen in Fig. 1). The conditions for no overlap to occur in the frequency domain for the rotating analyzer polarimeter areEq. (11) can be derived for other modulation schemes by applying a no side lobe overlap condition in the Fourier domain.
4. Unifying the PDRM and Linear Systems Methods for General Modulated Polarimeters
This section generalizes the linear systems formalism of section 3 to relate to the traditional PDRM method. The key result is that the PDRM method allows mid frequencies of the Stokes parameters to corrupt signal estimates through improper demodulation. This section demonstrates that many polarization artifacts can be remedied by choosing a different weighting scheme in formulating the PDRM.
Consider a polarized signal that is a function of space, time, and wavelength S(x, y, t, λ). The signal is measured with a polarimeter that contains a single detector with an integration window d(x, y, t, λ), a system impulse response function h(x, y, t, λ), and a polarimetric modulation described by A(x, y, t, λ). The modulated flux isEq. (12) are not necessarily linear, shift-invariant (LSI) functions. When the system is not LSI, the convolution integrals are replaced by overlap integrals of the more general form Eq. (12). In reality, the system is described by a polarimetric impulse response matrix that relates how the optical system alters the polarization state between object and image plane before it is ever sampled .Eq. (2). The W T W part of the pseudo inverse is the integral of the product of the modulation functions contained in A, so the inversion of this quantity describes how to separate the Stokes parameters. Z and Z −1 are diagonal matrices for modulation schemes where the modulators Aj are orthogonal over the integral. The quantity Z −1 will be referred to as the modulator inner product inversion matrix.
If there are an integer number of periods in all of the modulation functions contained in A, Z −1 will be constant with respect to the initial phase of the modulation. However, in cases where the polarimeter varies in time or in space, as would be the case when there is drift in the absolute angular position of a freely running rotating retarder or in the absolute phase of oscillation of the PEM-based systems , then in general Z will not be constant and needs to be computed separately at each reconstruction point. The more general Z in the time domain can be calculated byEq. (17) reduces to Eq. (16); in general arbitrary window shapes and modulation schemes need the weighted inner product matrix elements calculated for all space time and/or wavelength.
Next, to understand the W T term in Eq. (15), examine how it operates on the modulated flux in the standard PDRM formalism,
Using the linear systems formalism it is preferable to apply band limited low pass filters designed to eliminate the high frequency leakage. This is accomplished by separating the homodyne process from the low pass filter and applying the filter to the inverted signal according toEq. (19) A is a4 × 1 vector with each element a scalar function of space time and wavelength, not the 4 × N matrix that W conventionally implies. The quantity w(x, y, t, λ) is the windowing function in Eq. (17); this ensures that the reconstruction algorithm properly unfixes the modulators over the particular window that has been chosen for the estimation. Separating w from the inversion process allows for the optimization of the low pass filter operation and for the control over which portions of the frequency domain are included in the estimation. The advantage of this will become clear in the following section. In the special case when the Z matrix is constant over the modulation, the final estimation can be written as
This section contains examples of polarimeters measuring fluctuating scenes. This section considers polarimetric demodulation and high frequency leakage artifacts in the conventional PDRM and shows how to generalize the PRDM to eliminate them. Two signals with different bandwidths are shown in Fig. 2; the temporally wide signal ( ) satisfies the band limit requirement imposed by the polarimeter by Eq. (11)(a), while the temporally narrow signal ( ) has twice the frequency bandwidth as the polarimeter requirement WB and hence will have errors.
Section 5.2 analyzes a signal that satisfies the no overlap condition for the rotating retarder polarimeter that is similar to the condition discussed in Eq. (11) for the rotating analyzer polarimeter. Section 5.3 changes the signal to one that has twice the bandwidth of the signal in section 5.2.
5.1. Rotating Retarder Polarimeter
Consider a rotating retarder polarimeter that is free of noise and calibration error. Let the polarimeter be composed of an ideal linear retarder of retardance δ rotating at a frequency f 0 followed by an fixed ideal linear analyzer with the orientation 0° such that analyzer as function of time isEq. (21), let . Sample such that with and acquire 80 measurements centered on the excitation. The modulator inner product matrix for one period is Eq. (22) Z 12 = Z 21 ≠ 0 because the modulators A 0 and A 1 are not orthogonal; both have DC components.
5.2. Example: Band Limited Polarization Scene
The band limit criteria for the rotating retarder polarimeter of Section 5.1 that corresponds to the condition described by Fig. 1 is WB ≤ 2 f 0 and . Assume an excitation with the form in any one of the four Stokes parameters. The measurement of an arbitrary physical excitation can be decomposed into separate contributions from each Stokes component; Fig. 3 shows the contribution to the total measured flux for each Stokes component.
Figure 4 (Media 1) is animated showing how the both measured flux and the flux Fourier transform behave as the bandwidth increases; here Fig. 4(a) contains a single frame of the animation that shows the measured flux for the fully polarized signalFig. 3.
Figure 4(b) (Media 1) shows one frame of the animation of the Fourier transform as a function of bandwidth of the measured flux decomposed into the components arising from the individual modulated Stokes parameters shown in Fig. 3. The Fourier transform of the signal in Fig. 4(a) is obtained by summing all of the components in Fig. 4(b).
The first step in determining the time dependent Stokes parameters is to homodyne the measured flux with the functions given by A.
Figure 5 shows the Fourier transform of the homodyned flux A(t)I(t) using each of the four modulation functions in Eq. (21). The shaded region in Fig. 5 and all other following frequency domain plots indicates the band limit criteria for the modeled polarimeter. The first and second modulators place the s 0 and s 1 information in base band. The third modulator places the s 2 information at base band, and the fourth modulator places the s 3 information at base band.
Multiplication by the modulator inner product inversion matrix Z −1 separates s 0 and s 1 and equalizes the amplitude of all channels, yielding the result in Fig. 6.
Figure 6 shows how the time dependent Stokes parameter information has been placed at base band independently for each of the four Stokes parameters. After the signal has been demodulated there are still copies of both the desired signal and all other channels centered at various high frequency multiples of the first harmonic. If the filter choice for w does not reject the contributions from these copies, self-error (error in reconstruction resulting from the desired signal) and cross-error (error caused by channel cross talk) will occur. The artifacts caused by these copies are different from aliasing since there is no overlap in the frequency domain of these channels; aliasing will be discussed in section 5.3
The final step is low-pass filtering to extract the time dependent Stokes parameters. This is the step where most division of time polarimeter reconstruction strategies use the time-limited reconstruction window rather than a band limited strategy advocated here. By applying a band limited low pass filter, only the correct signals at baseband are allowed to pass. For this band limited input the filter perfectly reconstructs the full time-dependent input Stokes signal at base band using Eq. (20). In Fig. 6 it is seen that the ideal band limited filter given in the Fourier domain by , will reject all frequencies outside of the shaded base band region and provide artifact free reconstruction, with rect(x) = 1 for |x| < 1 and 0 otherwise.
In comparison Fig. 7 shows the effect of using a standard, 10-element rectangular window (as matrix notation in Eq. (5) implies)for corresponding to a sliding conventional PDRM with a width of 16 samples . Each of the four panels shows the Fourier transform of one of the Stokes parameters decomposed into the portions that arise from each of the inputs in Fig. 3. This filter choice leaks error at frequencies that were outside of the original band limit as well as attenuating the contrast of the parts of the spectrum that contained the desired signal.
In Fig. 7, substantial errors in the Fourier domain have been introduced. Using conventional data reconstruction only the DC term has been reconstructed error free. The error at high frequency manifests both as self-error (e.g. base band s 0 information showing up as high frequency error in s 0 signal) and cross error (e.g. base band s 0 information showing up as high frequency error in the s 1 signal), as well as base band signal attenuation.
Figure 8 shows the reconstructed Stokes parameters in the time domain, where the high frequency reconstruction error is evident. The results from a 16 element rect window are compared to the ideal reconstruction achieved by the band limited filter in Fig. 6. These artifacts have conventionally been described as being due to gradients in the signal .
5.3. Example: High Frequency Aliasing
Now consider the same rotating retarder polarimeter with a signal that has twice the band width (band width given by 2WB) as in Eq. (11) the previous section, (solid line in Fig. 2). The measured flux (I) is shown in Fig. 9(a), while the Fourier transform split into components are shown in Fig. 9(b). Figure 9 corresponds Fig. 4 (Media 1) from the previous example, and is another singal frame from the animation showing how the measured flux behaves as signal bandwidth increases.
The estimation using a band limited signal recovery algorithm (described in Eq. (20) with ) is shown in Fig. 10. The aliasing artifacts that occur even when processing with a band limited window (as in Fig. 10) demonstrate the resolution penalty incurred by using modulated polarimeters. In the case of the rotating retarder used in this example, the bandwidth requirement is compared to the Nyquist sampling requirement of for a polarization blind system. Since this signal has twice the required polarimeter bandwidth (the bandwidth is ) there are now unavoidable self aliasing and cross aliasing polarimeter artifacts present, compared to the band limited example which was only degraded by frequency leakage artifacts.
Figure 11 shows the time domain representation for both the conventional time limited and the band limited reconstruction window advocated here. In both cases, there is unavoidable cross channel aliasing in the base band signal that introduces polarization artifacts. In this case the time limited window might be more visually appealing but an application based metric would need to be used to compare the results for a particular task.
The theory of section 4 and the examples in section 5 demonstrate problems associated with traditional PDRM processing of modulated polarimeter data when the Stokes parameters are changing in time. The standard matrix formalism equally weights all of the observations in an N-element window in calculating the inversion of the measurement matrix W. Since the excitation in the conventional PDRM is assumed to be constant, equal weighting only guarantees proper reconstruction for constant signals. The band limited approach presented in this manuscript improves upon the conventional polarimetric data reduction matrix (time limited reconstruction) method for properly band limited time dependent Stokes parameters. When Eq. (11) is satisfied, error-free polarimetric reconstruction is possible for noise-free excitations. Equation (20) shows how to implement a band limited reconstruction algorithm for general modulated polarimeters. This result relaxes the requirement for high modulation frequencies to reduce error in DoT polarimeters [18, 19].
In section 5.3 a signal that was twice the polarimeter bandwidth criteria was considered; this example contained such high frequency fluctuations that polarization artifacts were unavoidable with either reconstruction method. Both algorithms presented in this manuscript produce significant error when applied to signals that contain fluctuations that are of higher frequency than the band width criteria determined by the polarimeter modulation. The choice of filter is much more complicated in the broader bandwidth example; increasing the bandwidth of the filter will allow more of the desired signal through, but will also pass more of the content from other channels. An optimal filter can be designed for a particular application based on the expected signal and polarimeter design.
References and links
2. L. J. Cheng, M. Hamilton, C. Mahoney, and G. Reyes, “Analysis of AOTF hyperspectral imaging,” in “Proceedings of SPIE Vol. 2231, Algorithms for Multispectral and Hyperspectral Imagery,” , A. Iverson, ed. (SPIE, Bellingham, WA, 1994), pp. 158–166.
4. M. P. Silverman and W. Strange, “Object delineation within turbid media by backscattering of phase modulated light,” Opt. Commun. 144, 7–11 (1997). [CrossRef]
5. D. B. Chenault and J. L. Pezzaniti, “Polarization imaging through scattering media,”(SPIE, Bellingham, WA, 2000), pp. 124 – 133.
6. Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Polarization-based vision through haze,” in “ACM SIGGRAPH ASIA 2008 courses,”(ACM, New York, NY, USA, 2008), SIGGRAPH Asia ’08, pp. 71:1–71:15
7. V. Thilak, D. G. Voelz, and C. D. Creusere, “Image segmentation from multi-look passive polarimetric imagery,” in “Proc. SPIE 6682,” , J. A. Shaw and J. S. Tyo, eds. (SPIE, Bellingham, WA, 2007), p. 668206. [CrossRef]
8. D. J. Diner, A. Davis, B. Hancock, G. Gutt, R. A. Chipman, and B. Cairns, “Dual-photoeleastic-modulator-based polarimetric imaging concept for aerosol remote sensing,” Appl. Opt. 46, 8428–8445 (2007). [CrossRef] [PubMed]
9. K. Sassen, “Polarization in LIDAR,” in “LIDAR: Range-resolved optical remote sensing of the atmosphere,” ,C. Weitkamp, ed. (Springer, 2005), pp. 19–42.
10. D. W. Tyler, A. M. Phenis, A. B. Tietjen, M. Virgen, J. D. Mudge, J. S. Stryjewski, and J. A. Dank, “First high-resolution passive polarimetric images of boosting rocket exhaust plumes,” in “Proc. SPIE vol. 7461: Polarization Science and Remote Sensing IV,” , J. A. Shaw and J. S. Tyo, eds. (SPIE, Bellingham, WA, 2009), p. 74610J.
11. R. A. Chipman, “Polarimetry,” in “Handbook of Optics,” , M. Bass, ed. (McGraw-Hill, 2009), 3rd ed.
12. R. M. A. Azzam, I. M. Elminyawi, and A. M. El-Saba, “General analysis and optimization of the four-detector photopolarimeter,” J. Opt. Soc. Am. A 5, 681–689 (1988). [CrossRef]
13. J. L. Pezzaniti and D. B. Chenault, “A division of aperture MWIR imaging polarimeter,” in “Proceedigns of SPIE vol. 5888: Polarization Science and Remote Sensing II,” , J. A. Shaw and J. S. Tyo, eds. (SPIE, Bellingham, WA, 2005), p. 5888OV.
14. A. G. Andreou and Z. K. Kalayjian, “Polarization imaging: principles and integrated polarimeters,” IEEE Sens. J. 2, 566–576 (2002). [CrossRef]
15. J. S. Tyo, C. F. LaCasse, and B. M. Ratliff, “Total elimination of sampling errors in polarization imagery obtained with integrated microgrid polarimeters,” Opt. Lett. 34, 3187–3189 (2009). [CrossRef] [PubMed]
17. K. Oka and T. Kato, “Spectroscopic polarimetry with a channeled spectrum,” Opt. Lett. 24, 1475–1477 (1999). [CrossRef]
18. R. M. E. Illing, “High-speed fieldable imaging stokes vector polarimeter,” in “Proceedigns of SPIE vol. 5888: Polarization Science and Remote Sensing II,” , J. A. Shaw and J. S. Tyo, eds. (SPIE, Bellingham, WA, 2005), p. 58880X.
19. L. Gendre, A. Foulonneau, and L. Bigué, “High-speed imaging acquisition of stokes linearly polarized components using a single ferroelectric liquid crystal modulator,” in “Proc. SPIE vol. 7461: Polarization Science and Remote Sensing IV,” , J. A. Shaw and J. S. Tyo, eds. (SPIE, Bellingham, WA, 2009), p. 74610G.
20. M. H. Smith, J. B. Woodruff, and J. D. Howe, “Beam wander considerations in imaging polarimetry,” in “Proceedings of SPIE vol. 3754, Polarization Measurement, Analysis, and Remote Sensing II,” , D. H. Goldstein and D. B. Chenault, eds. (SPIE, Bellingham, WA, 1999), pp. 50–54.
22. D. S. Sabatke, M. R. Descour, E. Dereniak, W. C. Sweatt, S. A. Kemme, and G. S. Phipps, “Optimization of retardance for a complete Stokes polarimeter,” Opt. Lett. 25, 802–804 (2000). [CrossRef]
23. J. S. Tyo and T. S. Turner, “Variable retardance, Fourier transform imaging spectropolarimeters for visible spectrum remote sensing,” Appl. Opt. 40, 1450–1458 (2001). [CrossRef]
24. A. Ambirajan and D. C. Look, “Optimum angles for a polarimeter: part I,” Opt. Eng. 34, 1651–1655 (1995). [CrossRef]
26. F. Goudail and A. Beniere, “Estimation precision of the linear degree of polarization and of the angle of polarization in the presence of different types of noises,” Appl. Opt. 49, 683–693 (2010). [CrossRef] [PubMed]
27. R. A. Chipman, “Polarization analysis of optical systems,” Opt. Eng. 28, 90–99 (1989).
28. R. A. Chipman, “Polarimetric impulse response,” in “Proc. SPIE col. 1317: Polarimetery: radar, Infrared, Visible, Ultraviolet, and X-Ray,”(SPIE, Bellingham, WA, 1990), pp. 223 – 241.
29. J. P. McGuire and R. A. Chipman, “Diffraction image formation in optical systems with polarization aberrations. I: formulation and example,” J. Opt. Soc. Am. A 7, 1614–1626 (1990). [CrossRef]