Abstract
A newly developed spectroscopic method called spatial heterodyne spectroscopy (SHS) characterized by grating space diffraction, compact size, high etendue, larger optical tolerance, and no moving parts. Spatial heterodyne spectroscopy has many uses, including atmospheric remote sensing, atmospheric composition research, and so on. However, due to the features of the sensor material and the operating environment, various noise will be introduced throughout the CCD image acquisition process. The imaging performance of the device will be reduced or possibly disabled as a result of these noise effects. We demonstrate how deep learning techniques may be used to denoise SHS images. Furthermore, we propose the use of N2N (noise2noise, noise to noise) method for the difficulty of deep learning to obtain a large number of target-noise pair. This method trains the deep learning network using only noisy images, which greatly reduces the difficulty of acquiring training data. We analyzed denoising data for monochromatic and continuous light. In terms of interferograms, the experimental results show that the deep neural network trained using this method can effectively recover interferogram image information, suppress noise, and maintain the continuity of the interference fringe edge. In terms of spectrograms, the deep neural network trained by this approach can suppress noise in monochromatic spectra and retrieve spectral signals damaged by noise in continuous spectra. As a result, the technique is suitable for spatial heterodyne spectroscopy denoising.
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