High-quality panchromatic image acquisition method for snapshot hyperspectral imaging Fourier transform spectrometer.

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Abstract

The acquisition of high-quality panchromatic images is vital to the multi-spectral images pan sharpening, especially to snapshot imaging spectrometers with a low spatial resolution. As an aperture-division snapshot imaging spectrometer, a snapshot hyperspectral imaging Fourier transform spectrometer has the characteristic that images of all the sub-apertures share almost the same spatial information with a small shift. With these sub-images, super-resolution is possible. In this paper, a high-quality panchromatic image acquisition method is proposed. A pre-trained deep learning network is utilized without enlarging the instrument size. The training dataset is obtained experimentally, and the network is designed to realize the contrast enhancement and super-resolution simultaneously. The experimental results demonstrate that the proposed method performs well in high-quality panchromatic image acquisition.