Phase retrieval (PR) is an important component in modern computational imaging systems. Recent advances in deep learning have introduced new possibilities for a robust and fast PR. An emerging technique called deep unfolding provides a systematic connection between conventional model-based iterative algorithms and modern data-based deep learning. Unfolded algorithms, which are powered by data learning, have shown remarkable performance and convergence speed improvement over original algorithms. Despite their potential, most existing unfolded algorithms are strictly confined to a fixed number of iterations when layer-dependent parameters are used. In this study, we develop a novel framework for deep unfolding to overcome existing limitations. Our development is based on an unfolded generalized expectation consistent signal recovery (GEC-SR) algorithm, wherein damping factors are left for data-driven learning. In particular, we introduce a hypernetwork to generate the damping factors for GEC-SR. Instead of learning a set of optimal damping factors directly, the hypernetwork learns how to generate the optimal damping factors according to the clinical settings, thereby ensuring its adaptivity to different scenarios. To enable the hypernetwork to adapt to varying layer numbers, we use a recurrent architecture to develop a dynamic hypernetwork that generates a damping factor that can vary online across layers. We also exploit a self-attention mechanism to enhance the robustness of the hypernetwork. Extensive experiments show that the proposed algorithm outperforms existing ones in terms of convergence speed and accuracy and still works well under very harsh settings, even under which many classical PR algorithms are unstable.

中文翻译：

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基于超网络的期望一致信号恢复算法的相位检索


相位检索（PR）是现代计算成像系统的重要组成部分。深度学习的最新进展为稳健、快速的 PR 带来了新的可能性。一种称为深度展开的新兴技术提供了传统的基于模型的迭代算法和现代基于数据的深度学习之间的系统连接。由数据学习驱动的展开算法与原始算法相比，表现出显着的性能和收敛速度的提高。尽管具有潜力，但大多数现有的展开算法在使用层相关参数时都严格限制在固定的迭代次数。在这项研究中，我们开发了一种新颖的深度展开框架，以克服现有的局限性。我们的开发基于展开的广义期望一致信号恢复（GEC-SR）算法，其中阻尼因子留给数据驱动学习。特别是，我们引入了一个超网络来生成 GEC-SR 的阻尼因子。超网络不是直接学习一组最优阻尼因子，而是学习如何根据临床设置生成最优阻尼因子，从而保证其对不同场景的适应性。为了使超网络能够适应不同的层数，我们使用循环架构来开发动态超网络，该动态超网络生成可以跨层在线变化的阻尼因子。我们还利用自注意力机制来增强超网络的鲁棒性。大量实验表明，该算法在收敛速度和精度方面优于现有算法，并且在非常恶劣的设置下仍然可以很好地工作，即使在许多经典 PR 算法不稳定的情况下。