Hyperspectral Image Reconstruction Using Deep External and Internal Learning
To solve the low spatial and/or temporal resolution problem which the conventional hypelrspectral cameras often suffer from, coded snapshot hyperspectral imaging systems have attracted more attention recently. Recovering a hyperspectral image (HSI) from its corresponding coded image is an ill-posed inverse problem, and learning accurate prior of HSI is essential to solve this inverse problem. In this paper, we present an effective convolutional neural network (CNN) based method for coded HSI reconstruction, which learns the deep prior from the external dataset as well as the internal information of input coded image with spatial-spectral constraint. Our method can effectively exploit spatial-spectral correlation and sufficiently represent the variety nature of HSIs. Experimental results show our method outperforms the state-of-the-art methods under both comprehensive quantitative metrics and perceptive quality.
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