Hologram conversion for speckle free reconstruction using light field extraction and deep learning.

A novel hologram conversion technique for speckle-less reconstruction is proposed. Many speckle-less reconstruction methods require holograms specially created for those techniques, limiting their applications to general pre-existing holograms. The proposed technique transforms an existing hologram with random phase distribution to new holograms for the application of the speckle-less reconstruction methods. The proposed technique first extracts a set of orthographic views from the existing hologram, then the extracted orthographic views are processed for the speckle noise removal using convolutional neural network. The processed orthographic views are finally used to synthesize new holograms with desired carrier waves by using non-hogel based computer generated hologram technique. The selection of the carrier wave is determined by the desired speckle-less reconstruction method. In this paper, we demonstrate the proposed technique with two speckle-less reconstruction methods; i.e. temporal speckle averaging of different random phase distributions and time-multiplexing of interleaved angular spectrums.