The application relates to a layered reconstruction imaging phase
recovery method and device based on an
object function implicit representation, wherein the method comprises the following steps: generating an implicit representation of an
object function by using a preset deep neural network; randomly sampling each pixel volume between adjacent slices, and calculating a corresponding projection potential function value according to the coordinates of the sampling points; performing forward
simulation of the projection potential function value for multi-slice layered imaging; defining a corresponding
loss function according to the difference between the
simulation data and experimental data; judging whether the
loss function meets a preset convergence condition; if the
loss function meets the preset convergence condition, sampling in a three-dimensional space position by using the implicit representation and performing phase
recovery of a three-dimensional
object function to obtain object function information meeting a preset optimal condition. The embodiment of the application utilizes the representation capability of a neural network and the function continuity of the neural network in a solution space, avoids the short board of a local optimization trap of a traditional method, and improves the accuracy of phase
recovery.