A Dual Stream Spectrum Deconvolution Neural Network

Abstract

With the development of spectral detection and photoelectric imaging, multiband spectrum is always degraded by the random noise and band overlap during the acquisition of spectrum devices. Owing to the fixed spectrum degradation model, the existing spectrum deconvolution technologies are sensitive to the handcrafted model designed and manually selected parameters. The fundamental cause of these limitations during spectral analysis is that spectral processing is limited by 1-D signal without structural information available and insufficient training samples. In this article, a dual stream neural network is proposed to reconstruct the original infrared spectroscopy, which effectively strengthens the capability to represent the feature of infrared spectrum. A novel activation function is proposed to realize the function of the dual stream network. Furthermore, a heuristic learning strategy from the perspective of balanced self-paced learning is exploited to help network train from simple to difficult, resolving the problem of high sample repeatability. Compared with other traditional methods, the experimental results show that our network can achieve state-of-the-art reconstruction result and fairly excellent performance in terms of the corresponding index within synectics and real spectrum experiments.