基于超网络的期望一致信号相位恢复算法
Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
摘要：
相位恢复是现代计算成像系统的重要组成部分。在过去的半个世纪里，已经开发了许多算法。深度学习的最新进展为强大而快速的PR引入了新的可能性。一种称为深度展开的新兴技术在传统的基于模型的迭代算法和现代的基于数据的深度学习之间提供了系统的连接。展开算法，这是由数据学习，已显示出显着的性能和收敛速度的改进，比原来的算法。尽管它们的潜力，大多数现有的展开算法被严格限制在一个固定的迭代次数时，层相关的参数使用。在这项研究中，我们开发了一个新的框架，深度展开，以克服现有的局限性。我们的开发是基于展开的广义期望一致信号恢复（GEC-SR）算法，其中阻尼因子被留给数据驱动的学习。特别地，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佳阻尼因子，而是学习如何根据临床设置生成最佳阻尼因子，从而确保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，我们使用一个经常性的架构来开发一个动态的超网络，生成一个阻尼因子，可以在线跨层变化。我们还利用自注意机制，以提高超网络的鲁棒性。大量的实验表明，该算法优于现有的收敛速度和精度方面，仍然可以很好地工作在非常苛刻的设置，即使在许多经典的PR算法是不稳定的。
英文摘要：
Phase retrieval (PR) is an important component in modern computational imaging systems. Many algorithms have been developed over the past half-century. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
