Optimization of Phase-Only Computer-Generated Holograms Based on the Gradient Descent Method
Shujian Liu, Yasuhiro Takaki
Abstract
The Gerchberg–Saxton (GS) algorithm is a Fourier iterative algorithm that can effectively optimize phase-only computer-generated holograms (CGHs). This study proposes a new optimization technique for phase-only CGHs based on the gradient descent method. The proposed technique evaluates the intensity distributions of reconstructed images to directly obtain the phase distributions of the CGHs, whereas the GS algorithm equivalently evaluates the amplitude distributions of reconstructed images and extracts phase distributions from complex-amplitude distributions of the holograms using a constant-amplitude constraint. The proposed technique can reduce the errors in the reconstructed images with fewer iterations than the GS algorithm.