高光谱图像包含着丰富的光谱信息,单幅RGB重建高光谱图像在**目标识别和医学诊断领域具有重要价值。传统算法无法对未知相机光谱响应的RGB图像进行重建,针对此问题,本文提出了一种基于改进残差密集网络的重建算法。首先,将改进的残差密集块作为残差密集网络的基本模块,使用自适应权重模块对特征通道进行特征重标定,使高光谱重建精度得到了提高。其次,用特征变换层替代原来网络的空间变换层,将解决图像超分辨率问题转换成解决高光谱重建问题,实现网络从空间维度到光谱维度的转变。实验结果表明:本文所提算法无论是在主观效果上还是在客观评估指标上均优于主流的传统方法和深度学习方法,与稀疏字典方法相比,本文算法的平均相对绝对误差(MRAE)和均方根误差(RMSE)分别下降了46.7%和44.8%。

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.