Optimizing speckles for dynamic objects using genetic algorithm in ghost imaging

Abstract

Different from the traditional imaging methods using first-order interference, ghost imaging (GI) uses the second-order correlation, bringing many potential applications. On the other hand, GI has been suffering from low efficiency in image reconstruction due to a high sampling rate, which is a barrier for its application, especially when dealing with dynamic objects. The genetic algorithm (GA) can optimize the speckle sequence for an object and enable GI reconstruction with a few speckle patterns. However, the optimized speckle sequence of the GA usually loses the generalization and can only reconstruct the object being tested, making it far from suitable for handling a dynamic object. Here, we propose an improved method based on the GA, where we make two selection rules: the selective patterns more likely have a high response from the object, and meanwhile, the selected patterns tend to be linearly independent from each other. The optimized speckle sequence under these rules not only results in successful reconstruction but also preserves a generalization to a certain extent, enabling the GI to reconstruct the different states of the dynamic object at a low overall sampling rate. In the verification of the first frame, our method performs better based on the demonstration of various algorithms. In a demonstration of the dynamic object at 50% sampling rate, the reconstructed images are 2.1775 dB higher at 12 different frames on average in the peak signal-to-noise ratio than the random speckle sequence.