High Generation Ability Research Articles

Color ghost imaging through the scattering media based on A-cGAN.

Ghost imaging plays an important role in the field of optical imaging. To realize color ghost imaging through the scattering media, we propose a deep learning method with high generation ability. Through our method, we can efficiently reconstruct color images with rich details, in line with human perception and close to the target color pictures. Experimental results show that our method can image through the scattering media with different scattering intensities and achieve good results even at a sampling rate of 0.1.

Spike sorting based on multi-class support vector machine with superposition resolution

A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.