Adaptive reconstruction imaging based on K-means clustering in off-axis digital holography

Abstract

An adaptive filtering method for off-axis digital holographic reconstruction is presented. The spatial spectrum distribution of an off-axis digital hologram is clustered based on the K-means clustering algorithm of unsupervised machine learning . The spatial-spectrum filtering can be carried out by the quantitative comparison of different filtering interception windows. The first-order spectrum including the object information can be automatically located and intercepted after clustering the spatial spectrum distribution of the hologram. The experiment results demonstrate that the reconstruction images with better resolution and imaging quality can be achieved by the adaptive filtering algorithm. This adaptive filtering reconstruction based on K-means clustering provides a new way of automatic reconstruction imaging for off-axis digital holography.