Abstract
A convolutional autoencoder for complete phase reconstruction in Digital Holographic Microscopy is reported. After proper training, this computationally efficient method reconstructs DHM holograms accurately when compared to the traditional approaches. This learning-based method is trained and validated with experimental samples of red blood cells.