Acta Optica Sinica, Volume. 41, Issue 7, 0730001(2021)
Hyperspectral Image Reconstruction Based on Improved Residual Dense Network
Hyperspectral images contain rich spectral information, and the hyperspectral image reconstruction from a single RGB image is of great value to military target recognition and medical diagnosis. Since traditional algorithms cannot reconstruct RGB images with unknown spectral response from cameras, this paper proposes a reconstruction algorithm based on an improved residual dense network. First, with an improved residual dense block as the basic module, we apply the adaptive weight module for feature recalibration, which improves the accuracy of hyperspectral image reconstruction. Additionally, our algorithm solves the hyperspectral image reconstruction instead of image super-resolution through replacing the spatial transformation layer with a feature one, which transforms the network from the spatial dimension to the spectral dimension. The experimental results show that the proposed algorithm is superior to the traditional methods and deep learning methods in both subjective effect and objective evaluation indicators. Compared with those of the sparse dictionary method, the mean relative absolute error (MRAE) and root mean square error (RMSE) of the proposed algorithm are reduced by 46.7% and 44.8%, respectively.
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Yong Li, Qiuyu Jin, Huaici Zhao, Bo Li. Hyperspectral Image Reconstruction Based on Improved Residual Dense Network[J]. Acta Optica Sinica, 2021, 41(7): 0730001
Category: Spectroscopy
Received: Jul. 28, 2020
Accepted: Nov. 12, 2020
Published Online: Apr. 11, 2021
The Author Email: Zhao Huaici (hczhao@sia.cn)