U2-Net-Combined End-to-End Unsupervised Learning Method for Implementing Accurate Computer-Generated Phase-Only Hologram

Holography is an emerging technology where phase-only spatial light modulator (SLM) is better than amplitude-only SLM because of its enhanced diffraction efficiency and rendering quality. However, phase-only interference pattern reconstruction is an ill-posed inverse problem, difficult to solve by standard supervised neural network approaches. Accurately representing the target input during reconstruction of phase-only hologram (POH) can become challenging given the inherent high spatial frequency of phase data and the complexity of its fringe patterns, expressing the inverse nature of the problem. Therefore, the model may be trained improperly, inducing major compromise in the predicted hologram’s quality. As a result, an unsupervised approach can be effective to resolve such a problem. This paper presents a novel combined U2-net-based computer-generated hologram (CUSNet-CGH) architecture, an end-to-end deep learning model comprising two distinctively designed combined convolution networks that accurately generate 2D POH. The proposed framework contains U2-net which is known for performing unsupervised learning tasks without relying on pre-trained backbones from labeled dataset, making it an ideal approach for conducting unsupervised training. The numerical reconstructions are compared with several other state-of-the-art methods such as holoencoder, holonet and CCNN-CGH to verify the enhanced quality. The average PSNR of the proposed end-to-end approach is as high as $\sim ~34$ dB with an average SSIM of 0.91, featured by DIV2K dataset, outperforming the state-of-the-art methods analyzed in the paper. Additionally, the time required to reconstruct the predicted output of the proposed model is verified as competitive, challenging the latest benchmarks, thanks to its minimized number of trainable parameters, and ensuring that the model is computationally lightweight. Finally, experiments also demonstrate that the proposed model yields high-quality optical reconstruction.