# Unfolding Framework with Complex-Valued Deformable Attention for High-Quality Computer-Generated Hologram Generation

## TL;DR

This paper introduces a deep unfolding network with complex-valued deformable attention for improved hologram generation, addressing interpretability, global context capture, and extended working distances, achieving state-of-the-art results.

## Contribution

It proposes a novel deep unfolding framework combining adaptive bandwidth-preserving modeling and complex-valued deformable attention for high-quality hologram reconstruction.

## Key findings

- Achieves PSNR over 35 dB on test data.
- Outperforms existing methods in accuracy and stability.
- Extends working distances beyond traditional ASM-based models.

## Abstract

Computer-generated holography (CGH) has gained wide attention with deep learning-based algorithms. However, due to its nonlinear and ill-posed nature, challenges remain in achieving accurate and stable reconstruction. Specifically, ($i$) the widely used end-to-end networks treat the reconstruction model as a black box, ignoring underlying physical relationships, which reduces interpretability and flexibility. ($ii$) CNN-based CGH algorithms have limited receptive fields, hindering their ability to capture long-range dependencies and global context. ($iii$) Angular spectrum method (ASM)-based models are constrained to finite near-fields.In this paper, we propose a Deep Unfolding Network (DUN) that decomposes gradient descent into two modules: an adaptive bandwidth-preserving model (ABPM) and a phase-domain complex-valued denoiser (PCD), providing more flexibility. ABPM allows for wider working distances compared to ASM-based methods. At the same time, PCD leverages its complex-valued deformable self-attention module to capture global features and enhance performance, achieving a PSNR over 35 dB. Experiments on simulated and real data show state-of-the-art results.