This paper uses traditional algorithms and deep learning algorithms to recover datacube obtained by CASSI and CSIMS in order to verify that CSIMS outperforms CASSI by comparing the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Relative spectral Quadratic Error (RQE) of the reconstructed datacube. The experimental results show that the datacube of CASSI and CSIMS can be both reconstructed by ADMM-TV algorithm which is the most effective among the traditional algorithms. PSNR of the reconstructed datacube of CASSI is 32.50 dB, while that of CSIMS is 35.53 dB, with an increase of 3.03 dB. By using deep learning algorithm, both systems improve substantially under the PnP-HSI network, with PSNR of CASSI growing to 38.85 dB and that of CSIMS growing to 41.97 dB, which can be seen that CSIMS is still 3.12 dB higher than CASSI.
In recent years, the snapshot multi-dimensional imaging technology is emerging and becomes an increasing research focus. Especially, the spatial, spectral and depth information of scenes is particularly useful in target detection, recognition, tracking, scene classification et al. This paper proposes a snapshot compressed light field imaging spectrometer based on compressed sensing with light field concept and a snapshot depth-spectral imaging architecture based on image mapping and light field to realize the capture of depth-spectral information simultaneously. Through simulations, we demonstrate that the proposed snapshot depth–spectral imaging systems are effective to measure the depth-spectral information of scenes in a single snapshot time.
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