Differentiable Forward Models for Holographic Display Systems

Abstract

Holographic displays are limited by experimental imperfections found in current spatial light modulators (SLMs). Some examples include phase quantization, optical aberrations, non-uniform illumination, and finite spectral bandwidth, all of which degrade reconstructed holographic image quality compared to ideal simulations. Compensating for these imperfections requires an accurate, differentiable forward model of the full optical pipeline from the SLM phase pattern to the captured holographic image. The first portion of this project develops a camera-in-the-loop (CITL) calibration and optimization framework, using real camera feedback to iteratively correct SLM phase patterns for hardware nonidealities such as optical distortions and misalignment. The second portion of the project extends this approach to a simulation-only setting by training a learned differentiable proxy model (PLMPropagate) against a physically grounded simulator (PLMSimulator), incorporating Zernike aberrations, non-uniform illumination, detector noise, and Gaussian spectral bandwidth. Phase patterns are then re-optimized through the learned proxy to account for these imperfections without requiring physical hardware access. Evaluated on ten test images, the proxy-optimized phase achieves an average improvement of +2.58 dB PSNR and +0.109 SSIM over the naive baseline, demonstrating that learned forward models can substantially improve holographic image quality even in the absence of a physical camera-in-the-loop.