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Alternative TitleHyperspectral Images Reconstruction Based on Improved Residual
李勇1; 金秋雨1,2; 赵怀慈2; 李波3
Source Publication光学学报
Contribution Rank2
Funding Organization辽宁省自然科学基金(2019-ZD-0205) ; 辽宁省自然科学基金(2019-MS-238)
Keyword高光谱成像 残差密集网络 通道自适应 特征重标定 RGB图像


Other Abstract

Hyperspectral images contain rich spectral information. The hyperspectral images reconstruction from a single RGB image is of great value in the field of military target recognition and medical diagnosis. Traditional algorithms cannot reconstruct RGB images with unknown camera spectral response. To solve this problem, an improved residual dense network is proposed. Using an improved residual dense block as the basic module of the residual dense network and the feature channels are recalibrated by the auto-adaptive weight module, which improves the accuracy of hyperspectral reconstruction. Additionally, the feature transformation layer is used to replace the spatial transformation layer of the original network, which converts the problem of image super-resolution to the problem of hyperspectral reconstruction, and realizes the transformation of the network from the spatial dimension to the spectral dimension. The experimental results demonstrate that our proposed method is superior to the existing traditional methods and deep learning methods in both subjective effect and objective evaluation indicators. Compared with the sparse dictionary method, the Mean Relative Absolute Error (MRAE) and Root Mean Square Error (RMSE) are reduced by 46.7% and 44.8% respectively.

Document Type期刊论文
Corresponding Author赵怀慈
Recommended Citation
GB/T 7714
李勇,金秋雨,赵怀慈,等. 基于改进残差密集网络的高光谱重建[J]. 光学学报,2020:1-15.
APA 李勇,金秋雨,赵怀慈,&李波.(2020).基于改进残差密集网络的高光谱重建.光学学报,1-15.
MLA 李勇,et al."基于改进残差密集网络的高光谱重建".光学学报 (2020):1-15.
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