Reconstruction quality evaluation of compressed sensing image mapping spectrometer

Shuya Yang; Xiaoming Ding; Hao Yuan; Dunqiang Lu; Qiangqiang Yan

doi:10.1117/12.3007797

18 December 2023 Reconstruction quality evaluation of compressed sensing image mapping spectrometer

Shuya Yang, Xiaoming Ding, Hao Yuan, Dunqiang Lu, Qiangqiang Yan

Proceedings Volume 12967, AOPC 2023: Computing Imaging Technology; 129670A (2023) https://doi.org/10.1117/12.3007797
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuya Yang, Xiaoming Ding, Hao Yuan, Dunqiang Lu, and Qiangqiang Yan "Reconstruction quality evaluation of compressed sensing image mapping spectrometer", Proc. SPIE 12967, AOPC 2023: Computing Imaging Technology, 129670A (18 December 2023); https://doi.org/10.1117/12.3007797

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