• Infrared and Laser Engineering
  • Vol. 51, Issue 3, 20210231 (2022)
Hong Cheng, Li Wang, Rui Wang, Xinyu Xiang, Quanbing Zhang, and Xiaotian Zhu
Author Affiliations
  • National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
  • show less
    DOI: 10.3788/IRLA20210231 Cite this Article
    Hong Cheng, Li Wang, Rui Wang, Xinyu Xiang, Quanbing Zhang, Xiaotian Zhu. Phase retrieval based on the transport of intensity equation under adaptive focus[J]. Infrared and Laser Engineering, 2022, 51(3): 20210231 Copy Citation Text show less

    Abstract

    The non-interference phase retrieval method based on the transport of intensity equation was a method to obtain the phase by solving the intensity images. In the process of image acquisition, the selection of in-focus image was very important. But it was usually determined by subjective methods, which led to inaccurate in-focus positioning, thus affecting the accuracy of phase results. Firstly, an phase retrieval method based on the transport of intensity equation under adaptive focus was proposed; Secondly, the edge duty ratio was used to locate the acquired images in this algorithm. After solving the phase, the optimal focus position was located when the edge duty ratio locating position kept unchanging by the circular angular spectrum propagation; Finally, the phase of the sample was solved by using the transport of intensity equation. The result show that this algorithm not only improved the accuracy of phase retrieval, but also reduced the time to obtain a large number of images. In the simulation experiment, the correlation coefficient between the retrieval phase and the original phase reached 0.9866, and the RMSE error is 0.3050. In the actual experiment of microlens array, the error between the true height of the microlens and the height solves by the phase retrieval method proposed is only 5.7%, which proves that the algorithm can locate the optimal focus position in the field of microscopic imaging. And the algorithm is conducive to the development of auto-focus technology and improves the accuracy of phase retrieval.
    ${\left. {\frac{{\partial I(x,y,{\textit{z}})}}{{\partial {\textit{z}}}}} \right|_{{\textit{z}} = {{\textit{z}}_0}}} = - \frac{\lambda }{{2\pi }}\nabla \cdot (I(x,y,{{\textit{z}}_0})\nabla \phi (x,y,{{\textit{z}}_0}))$(1)

    View in Article

    ${\left. {\frac{{\partial I(x,y,{\textit{z}})}}{{\partial {\textit{z}}}}} \right|_{{\textit{z}} = {{\textit{z}}_0}}} \approx \frac{{I(x,y,{{\textit{z}}_0} + \Delta {\textit{z}}) - I(x,y,{{\textit{z}}_0} - \Delta {\textit{z}})}}{{2\Delta {\textit{z}}}}$(2)

    View in Article

    $\begin{gathered} \frac{{I(x,y,{{\textit{z}}_0} + \Delta z) - I(x,y,{{\textit{z}}_0} - \Delta {\textit{z}})}}{{2\Delta {\textit{z}}}} = \\ \frac{\lambda }{{2\pi }}\nabla \cdot (I(x,y,{{\textit{z}}_0})\nabla \phi (x,y,{{\textit{z}}_0})) \\ \end{gathered} $(3)

    View in Article

    ${\phi _{out}}(x,y,{{\textit{z}}_0}) = {\phi _{in}}(x,y,{{\textit{z}}_0}) + \phi (x,y,{{\textit{z}}_0})$(4)

    View in Article

    $\eta {\rm{ = }}\frac{{{N_B}}}{{{N_T} - {N_B}}}$(5)

    View in Article

    $ \left\{ {\begin{array}{*{20}{l}} \begin{gathered} {g_x} = \left[ {f\left( {i + 1,j - 1} \right) + f\left( {i + 1,j} \right) + f\left( {i + 1,j + 1} \right)} \right] - \\ \left[ {f\left( {i - 1,j - 1} \right) + f\left( {i - 1,j} \right) + f\left( {i - 1,j + 1} \right)} \right] \\ \end{gathered} \\ \begin{gathered} {g_y} = \left[ {f\left( {i - 1,j + 1} \right) + f\left( {i,j + 1} \right) + f\left( {i + 1,j + 1} \right)} \right] -\\ \left[ {f\left( {i - 1,j - 1} \right) + f\left( {i,j - 1} \right) + f\left( {i + 1,j - 1} \right)} \right] \\ \end{gathered} \\ {\nabla f = \sqrt {{g_x}^2 + {g_y}^2} } \end{array}} \right. $(6)

    View in Article

    $\begin{split} \\ \frac{{\partial I(x,y,{\textit{z}}_0')}}{{\partial {\textit{z}}}} \approx \frac{{I(x,y,{\textit{z}}_{_0}' + \Delta {\textit{z}}) - I(x,y,{\textit{z}}_{_0}' - \Delta {\textit{z}})}}{{2\Delta {\textit{z}}}} \end{split}$(7)

    View in Article

    $ {\nabla }^{2} =-\frac{\lambda }{2\text{π} }\frac{\partial I(x,y,{z}_{0}^{{'}})}{\partial z}$(8)

    View in Article

    $\nabla f(x,y) = ix{F^{ - 1}}{f_x}F[f(x,y)] + iy{F^{ - 1}}{f_y}F[f(x,y)]$(9)

    View in Article

    $ {\nabla }^{2}f(x,y)=-{F}^{-1}({f}_{x}^{2}+{f}_{y}^{2})F[f(x,y)]$(10)

    View in Article

    $\varphi (x,y,{\textit{z}}_0') = {F^{ - 1}}(f_x^2 + f_y^2)F\left[ {\frac{\lambda }{{2\pi }}\frac{{\partial I(x,y,{\textit{z}}_0')}}{{\partial {\textit{z}}}}} \right]$(11)

    View in Article

    $ \begin{split} \phi (x,y,{{\textit{z}}_0}'){\rm{ = }}&{F^{ - 1}}[{(f_x^2 + f_y^2)^{ - 1}}] \cdot {F^{ - 1}}\\ &\left\{ {\nabla [{I^{ - 1}}(x,y,{{\textit{z}}_0}')\nabla \varphi (x,y,{{\textit{z}}_0}')]} \right\} \end{split} $(12)

    View in Article

    $RMSE = \sqrt {\dfrac{{\displaystyle\mathop \sum \limits_{x,y} {{({\phi _r} - \phi )}^2}}}{{M \times N}}} $(13)

    View in Article

    $SSIM(X,Y) = \frac{{(2{\mu _X}{\mu _Y} + {C_1})({\sigma _{XY}} + {C_2})}}{{(\mu _X^2 + \mu _Y^2 + {C_1})(\sigma _X^2 + \sigma _Y^2 + {C_2})}}$(14)

    View in Article

    Hong Cheng, Li Wang, Rui Wang, Xinyu Xiang, Quanbing Zhang, Xiaotian Zhu. Phase retrieval based on the transport of intensity equation under adaptive focus[J]. Infrared and Laser Engineering, 2022, 51(3): 20210231
    Download Citation