Spectral Reconstruction Using Residual Channel Affinity Propagation Network with Structural Similarity Constraint

Recently, deep convolutional neural networks (CNNs) have been widely exploited for spectral reconstruction (SR) and achieved significant promotion. Nevertheless, most of the previous studies paid much attention to the design of the depth and width of the network, and neglected to explore the correlation between the intermediate feature maps, which hindered the representational ability of CNNs. To mitigate this problem, we propose a deep residual channel affinity propagation network (RCAPN) to learn the affinity matrix for more powerful feature expression. Specifically, the backbone consists of several dual residual blocks (DRB) with long and short skip connections to bypass plentiful low-frequency information. Furthermore, a novel channel affinity propagation module (CAPM) embedded in the DRB is investigated to learn the affinity among channels and adaptively integrate interdependencies to strengthen feature representations. Finally, a structural similarity (SSIM) constraint is employed to capture the structural information and recover more accurate edge positions. Experimental results demonstrate the superior performance of our proposed algorithm.