Simultaneous measurement of multidimensional displacements using digital holographic interferometry involves multi-directional illumination of the deformed object and requires the reliable estimation of the resulting multiple interference phase distributions. The paper introduces an elegant method to simultaneously estimate the desired multiple phases from a single fringe pattern. The proposed method relies on modeling the reconstructed interference field as a piecewise multicomponent polynomial phase signal. Effectively, in a given region or segment, the reconstructed interference field is represented as the sum of different components i.e. complex signals with polynomial phases. The corresponding polynomial coefficients are estimated using the product high-order ambiguity function. To ensure proper matching of the estimated coefficients with the corresponding components, an amplitude based discrimination criterion is used. The main advantage of the proposed method is direct retrieval of multiple phases without the application of spatial carrier based filtering operations.
© 2011 OSA
The measurement of in-plane and out-of-plane displacements of a deformed object is required for many applications. For such multidimensional measurements in digital holographic interferometry (DHI), the diffuse object is illuminated along different directions  resulting in multiple object beams. For each illumination direction, there is an associated sensitivity vector and interference phase. The in-plane and out-of-plane displacements are related to the multiple phases for different directions through the corresponding sensitivity vectors. Hence, estimation of these multiple interference phases is required for multidimensional deformation measurements. For a multi-beam illumination setup, methods [2–4] based on phase shifting technique have been proposed in DHI, but they are not suitable for simultaneous measurements because of sequential illumination and further require multiple frames.
For simultaneous multidimensional measurements, various methods [1, 5–7] have been developed in DHI. In these methods, the desired multiple phases are estimated using filtering operations based on the application of a spatial carrier. However, such approach requires careful control of the carrier in the experimental setup to ensure sufficient spectral separation, which might be difficult in practice. In addition, the phases obtained after filtering are of wrapped form and thus require additional complex unwrapping operations which are time-consuming and susceptible to noise.
In the paper, we propose an approach that is capable of estimation of multiple phases from a single fringe pattern in DHI without the requirement of an additional spatial carrier. The proposed method relies on piecewise multicomponent polynomial phase signal formulation. The theory of the proposed method is discussed in the next section. The analysis of the method is presented in section 3 followed by conclusions.
To explore the proposed method, the illumination of a diffuse object by two beams as shown in Fig. 1 is considered. In DHI, with a reference wave and two object waves resulting from the dual-beam illumination, the reconstructed interference field is obtained as Eq. (1) is a challenging problem since Γ(x,y) is the sum of two complex signals. The applicability of the popular spatial fringe analysis methods based on Fourier transform , windowed Fourier transform  and wavelet transform  is limited to extracting a single phase distribution from a fringe pattern and their accuracy would be strongly affected for the retrieval of dual phases due to the spectral overlap of the two complex signals in absence of a spatial carrier. Similarly, the recently developed methods based on polynomial phase approximation [11–13] are also affected when multiple phases are present.
In the proposed method, the reconstructed interference field Γ(x,y) is modeled as a piecewise multicomponent polynomial phase signal to directly estimate the phases Δϕ1(x, y) and Δϕ2(x, y). Accordingly, a given column/row of Γ(x,y) is divided into say Nw segments and the phase of each component in every segment is approximated as a polynomial. Hence, for a given column x and segment i where i ∈ [1,Nw], we haveEquation (4) represents a multicomponent polynomial phase signal of order M and the phases Δϕ1i(y) and Δϕ2i(y) in the ith segment can be obtained from the polynomial coefficients [ai0, ··· ,aiM] and [bi0, ··· ,biM]. To estimate these coefficients, product high-order ambiguity function (PHAF)  is used. The main advantage of using PHAF lies in its ability to suppress the unwanted interference caused due to multiple components which leads to robust estimation of the desired coefficients.
The mth order PHAF is computed through the following steps :
- The above steps are repeated for K different lag parameters i.e. [τ1,m−1, ··· ,τK,m−1] to obtain different HAFs corresponding to k ∈ [1,K].
To comprehend the working of PHAF, consider a multicomponent signal with cubic phases i.e. M = 3,14] and hence, we used the integer values of [τ1,2,τ2,2,τ3,2] = [N/3, N/3 – 10,N/3+10] for K = 3, m = 3 and [τ1,1,τ2,1] = [N/2 – 10,N/2+ 10] for K = 2, m = 2 in the analysis.
To obtain the highest order coefficients i.e. a3 and b3, the third order (m = 3) PHAF is computed and its spectrum |XK,m(ω)| is shown in Fig. 2(a) for the case of equal amplitudes i.e. A1 = A2. As the mth order polynomial coefficient is related to the frequency through Eq. (9), all PHAF plots in the paper are shown on a frequency scaled axis, i.e. instead of ω so that the coefficients am and bm directly correspond to the peaks in the PHAF spectrum. From Fig. 2(a), it is clear that two distinct peaks are visible corresponding to and 0.00001 i.e. the third order coefficients.
However, for the equal amplitude case, the main difficulty lies in matching the estimated coefficients with their respective components i.e. deciding whether the peak corresponds to a3 or b3. To resolve this, unequal amplitudes could be used so that the PHAF peak corresponding to the stronger component is dominant. This is shown in Fig. 2(b) and Fig. 2(c) for the cases A1 = 1, A2 = 1.5 and vice-versa. Consequently, mth order PHAF peak detection would yield the mth order polynomial coefficient for the stronger component.
For the case A1 = 1.5, A2 = 1 i.e. A1 > A2, tracing the peak of the third order (m = 3) PHAF spectrum as shown in Fig. 2(c) provides the estimate â3 for the stronger component. Subsequently, to obtain the second order coefficient a2, a peeling operation is carried out to decrement the polynomial order of the stronger component,Fig. 2(d) where the peak corresponds to â2. Subsequently, to estimate the first order coefficient a1, an additional peeling operation is carried out, Fig. 2(e) where the peak corresponds to â1. Finally, the contribution of first order coefficient is removed from Γ1(y) to obtain,
The above procedure estimates the first or stronger component’s polynomial coefficients iteratively from the highest order to the lowest. The estimates are [â3, â2, â1, â0, Â1] = [−0.00002,0.008,0.5001,−0.0650,1.4956]. Effectively, all the parameters i.e. the polynomial coefficients and the amplitude of the first component are now known. Subsequently, the contribution of the first component is removed from Γ(y) to obtain the second component,Fig. 3(a), exhibits a dominant peak at which corresponds to the third order coefficient b3. The remaining coefficients for Γ′(y) are obtained in the same way as for the stronger component using successive polynomial order reduction by peeling and corresponding PHAF peak detection. The PHAF spectra for m = 2 and m = 1 are shown in Fig. 3(b) and Fig. 3(c). The estimated coefficients for the second component are [b̂3, b̂2, b̂1, b̂0] = [0.00001, −0.0060,−0.5001,−0.0562].
The phases for the first and second components are constructed from the polynomial coefficients as in Eq. (2) and Eq. (3). The original vs estimated first phase in radians is shown in Fig. 2(f). Similarly, the original vs estimated second phase in radians is shown in Fig. 3(d). The corresponding estimation errors in radians for the first and second phases are shown in Fig. 3(e) Fig. 3(f).
The above procedure, though shown for a third order multicomponent signal, can be easily generalized for other polynomial orders. For the Mth order multicomponent signal Γi(y) of Eq. (4), the two sets of polynomial coefficients i.e. [ai0, ··· ,aiM] and [bi0, ··· ,biM] in the ith segment are computed using the PHAF based estimation scheme discussed above. To minimize the errors due to noise and cross-component influence, these coefficient estimates are further refined using a multicomponent optimization routine based on Nelder-Mead simplex algorithm . Using these coefficients, the multiple phases are constructed for the ith segment using Eq. (2) and Eq. (3). Though the estimates obtained along a column are unwrapped, a phase-stitching procedure  between adjacent columns is used to obtain continuous phase distributions.
It needs to be emphasized that the amplitude based separability criterion ensures proper matching of the estimated coefficients to the respective components in each segment. From an experimental perspective, the amplitude inequality between the two components could be achieved in DHI by controlling the relative intensities of the individual object beams.
Finally, the proposed method to retrieve multiple phase distributions can be summarized as
- Each column of the reconstructed interference field Γ(x,y) is divided into Nw segments to obtain Γi(y).
- In each segment, Γi(y) is modeled as a multicomponent polynomial phase signal as in Eq. (4).
- The second component is obtained by removing the contribution of the first component from Γi(y) using Eq. (16).
- The coefficients [bi0, ··· ,biM] of the second component are estimated in a similar manner as step 3.
- The above steps are repeated for all segments and subsequently for all columns to obtain the overall multiple phase distributions.
To analyze the proposed method, a reconstructed interference field with multiple phases was simulated in MATLAB,Fig. 4(a) and Fig. 4(b). The real part of Γ(x,y) which constitutes the fringe pattern (256 × 256 pixels) is shown in Fig. 4(c).
To highlight the problem of spectral overlap in the absence of a spatial carrier, the Fourier transform of Γ(x,y) was calculated as,Fig. 4(d). From the figure, it is clear that the spectra of the two components are overlapped and not well separated. Due to the spectral overlap, the individual components with the phases Δϕ1(x, y) and Δϕ2(x, y) cannot be efficiently filtered in the frequency domain using the Fourier transform. Hence, in the absence of a spatial carrier, retrieval of multiple phases is difficult using current phase estimation methods.
The proposed method was applied to estimate the phases Δϕ1(x, y) and Δϕ2(x, y) from Γ(x,y). For the analysis, we used Nw = 4 and M = 2. The estimated first phase in radians is shown in Fig. 5(a). The corresponding phase estimation error in radians is shown in Fig. 5(b). Similarly, the estimated second phase in radians is shown in Fig. 5(c). The corresponding phase estimation error in radians is shown in Fig. 5(d). The root mean square errors (RMSE) for the estimation of Δϕ1(x, y) and Δϕ2(x, y) were 0.0256 and 0.0333 radians. From the analysis, it is clear that the proposed method is capable of reliable estimation of multiple phases from a single fringe pattern.
The paper presents a piecewise multicomponent polynomial phase approximation based method for multiple phase retrieval which is desired for the simultaneous in-plane and out-of-plane deformation measurements in DHI. The major advantage of the proposed method lies in its ability to extract information about multiple phases from a single fringe pattern without the need of spatial carrier based spectral filtering, which is hitherto not possible with the current state-of-the-art spatial fringe analysis methods. In addition, the method provides continuous phase distributions and does not require complex unwrapping algorithms. We believe that the proposed method would enhance the applicability of DHI for simultaneous multidimensional measurements.
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