A study on denoising with deep convolutional neural networks in spatial heterodyne spectroscopy

Abstract

Spatial heterodyne spectroscopy is a hyperspectral remote sensing technology. With the continuous improvement of signal detection accuracy, new methods are urgently needed to further reduce the interference of noise on the information contained in spatial heterodyne interferograms. Convolutional neural networks, as an emerging field, have achieved good results in image denoising in recent years. This paper applies convolutional neural networks to the field of spatial heterodyne spectroscopy, constructs and trains four deep convolutional neural network models for denoising spatial heterodyne interferograms under different brightness and Gaussian noise conditions, and compares their performance with other algorithms. The results show that deep convolutional neural networks have significant advantages in denoising spatial heterodyne interferograms. Finally, a comparison of four neural networks was conducted to explore how to reasonably select network models. This work provides new ideas and effective solutions for reducing the interference of noise on spatial heterodyne spectral information and improving signal detection accuracy.