Compressive hyperspectral imaging based on Images Structure Similarity and deep image prior

In this paper, the structural similarity between RGB image and spectral image is studied, and a non-iterative Images Structure Similarity(ISS) method for fast reconstruction of spectral image is proposed. At the same time, the input of the Deep Image Prior (DIP) method is optimized for the first time by using the initial spectral data reconstructed by ISS. It raises the starting value of the iteration. Specifically, we take the RGB image data as the base of the spectral data, and solve the base coefficient by the least square method to quickly estimate the initial hyperspectral image. The Gaussian noise data is replaced by the estimated initial spectral data to constrain the solution space of the network and reduce the number of iterations. Finally, the structural similarity between RGB image and spectral image is used. The RGB three-channel graph is used to filter the iterative results to improve the reconstruction quality. Experimental results show that compared with other hyperspectral imaging methods, the proposed method can improve the quality of reconstruction in both spectral resolution and spatial resolution. In addition, compared with other methods based on Deep Image Prior (DIP), our improvement greatly reduces the reconstruction time and is more suitable for actual snapshot spectral imaging.

Automated summary

This paper studies the structural similarity between RGB and spectral images and proposes a non-iterative Images Structure Similarity (ISS) method for fast reconstruction of spectral images. Additionally, the input of the Deep Image Prior (DIP) method is optimized for the first time by using the initial spectral data reconstructed by ISS, leading to an improved starting value for the iteration. The experimental results show that the proposed method can enhance the reconstruction quality in both spectral and spatial resolutions, while significantly reducing the reconstruction time compared to other DIP-based methods.