DOLPHIn - Dictionary Learning for Phase Retrieval
This work addresses phase retrieval with unknown sparsity for imaging applications, representing an incremental improvement by integrating dictionary learning into existing sparse prior frameworks.
The authors tackled the problem of reconstructing images from noisy phase-retrieval measurements by jointly learning a dictionary and recovering the signal, achieving significantly better reconstructions than methods that cannot exploit hidden sparsity.
We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase. Specifically, we consider the task of estimating a two-dimensional image from squared-magnitude measurements of a complex-valued linear transformation of the original image. Several recent phase retrieval algorithms exploit underlying sparsity of the unknown signal in order to improve recovery performance. In this work, we consider such a sparse signal prior in the context of phase retrieval, when the sparsifying dictionary is not known in advance. Our algorithm jointly reconstructs the unknown signal - possibly corrupted by noise - and learns a dictionary such that each patch of the estimated image can be sparsely represented. Numerical experiments demonstrate that our approach can obtain significantly better reconstructions for phase retrieval problems with noise than methods that cannot exploit such "hidden" sparsity. Moreover, on the theoretical side, we provide a convergence result for our method.