Quaternionic Reweighted Amplitude Flow for Phase Retrieval in Image Reconstruction
This work addresses phase retrieval for color image reconstruction, offering incremental improvements in algorithms for managing color signals.
The authors tackled the quaternionic phase retrieval problem for color image reconstruction by proposing novel algorithms, including Quaternionic Reweighted Amplitude Flow (QRAF) and its variants, which significantly improved recovery performance and computational efficiency compared to state-of-the-art methods.
Quaternionic signal processing provides powerful tools for efficiently managing color signals by preserving the intrinsic correlations among signal dimensions through quaternion algebra. In this paper, we address the quaternionic phase retrieval problem by systematically developing novel algorithms based on an amplitude-based model. Specifically, we propose the Quaternionic Reweighted Amplitude Flow (QRAF) algorithm, which is further enhanced by three of its variants: incremental, accelerated, and adapted QRAF algorithms. In addition, we introduce the Quaternionic Perturbed Amplitude Flow (QPAF) algorithm, which has linear convergence. Extensive numerical experiments on both synthetic data and real images, demonstrate that our proposed methods significantly improve recovery performance and computational efficiency compared to state-of-the-art approaches.