The image retrieval algorithm of ptychography is a kind of conventional gradient descent algorithm. This algorithm begins with a set of guessing functions, and the signals collected in the experiment are continuously input into the iteration process to achieve final convergence. However the reconstructed process often falls into stagnation because of the low gradient around the local minima and the extent of the gradient descent is too small.

This study aims to solve the convergence stagnation problem of conventional gradient descent algorithm by using momentum ptychographical iterative engine (mPIE).

The concept of “momentum” was introduced into the ptychography algorithm to update conventional algorithm, called the momentum ptychographical iterative engine (mPIE). Adding momentum to an algorithm means that the iterative process has the characteristic of inertance. The extent of gradient descent relies on the previous steps and the gradient of current step. With the momentum, convergence can escape stagnation, and the reconstructed process can continue even around the local minima with a gradient is very small. The same set of coherent diffraction imaging experimental data was used to test the reconstructed image quality of mPIE algorithm and compare with that of extending ptychographical iterative engine (ePIE) algorithm.

The experimental results showed that mPIE algorithm outperformed ePIE algorithm with smaller convergence value of objective function, and better final reconstructed image quality.

The result indicates that a better reconstructed result can be achieved with mPIE algorithm compared with the traditional ePIE algorithm.