Fig. 5: Phase retrieval of a two-dimensional vortex in the presence of noise, comparing RRR and the gain-based system.
From: Phase retrieval via gain-based photonic XY-Hamiltonian optimization
The phase-retrieval problems were constructed with L = 5 phase filters. The size of the vortex core ξ = 1, and the vortex field were discretised into a 10 × 10 grid. a Each panel displays the reconstructed sample vector \(\widetilde{{{\bf{x}}}}\), where the grayscale image encodes amplitude, and the color image encodes phase. For each noise level, both RRR and the gain-based method start from the same initial condition. Here, RRR runs for 10,000 iterations, while the gain-based system is evolved to t = 1000. b The ground-truth sample vector x that describes a 2D vortex, showing amplitude (left) and phase (right). c Phase-retrieval error (RSE) versus the signal-to-noise ratio (SNR). In this test, the noisy observation vectors \(\widetilde{{{\bf{b}}}}\) presented to the solvers contained Gaussian noise. Each data point represents the average of 20 random instances, where the vortex core is placed at different positions, and each algorithm is initialized randomly. Error bars denote the standard deviation of the final RSE values. See “Fig. 5” table in supplementary data.
