###### Abstract
In [30], a pyramid fully convolutional network made up of an encoder sub-network and a pyramid fusion sub-network are proposed to refine the spatial information of the multispectral image in a global-to-local manner. Ref. [31] proposes an HS and MS image fusion method (called as CNN-Fus), which is based on subspace representation and CNN denoiser. Ref. [32] proposes a novel variational probabilistic autoencoder framework implemented by CNN in order to fuse the spatial and spectral information contained in the low-resolution (LR) HS and high-resolution (HR) MS images. This method is called FusionNet. To encode spatial and spectral distortion, Ref. [33] proposes a complex multi-scale fusion neural network, termed HAM-MFN, which designs two branches to extract features of low-resolution HS and MS images, respectively, and then fuse them at different scales. In [34], a quadratic optimization network with matrix decomposition is constructed, and the fusion problem is substituted by the optimization problem for spectrum and space with a customized loss function. Finally, in [35], authors propose a new framework, called recurrent attention fusion network (abbreviated to RAFnet) to obtain high-resolution HS images in an unsupervised manner. In pan-sharpening area, the detail-injection-based methods usually show high color preservation ability and also have considerable potential in hyper-sharpening tasks [36] naturally. Meanwhile, deep learning, especially the convolutional neural network, offers impressive performance of non-linear and local-structure feature extraction in the computer vision field. Therefore, inspired by the detail-injection pan-sharpening methods proposed in [37], in this paper, we propose a coupled CNN-based detail injection method (abbreviated to CpCNN) for HS and MS image fusion. The proposed method employs a couple of convolutional neural networks for high-frequency detail feature extraction and prediction of HS and MS images, respectively. Compared with conventional detail injection methods, a CNN-based model learns the spatial structure information from the pending images in an automatic and supervised fashion, thereby circumventing the intermediate process of separately estimating the details and injection gains and reducing the model uncertainty. The main contributions of this paper are as follows: (1) it is the first effort that incorporates a CNN network with a detail-injection model for the hyper-sharpening task, which would contribute to a substantial improvement in spectral fidelity; (2) in contrast with pan-sharpening works, we use a couple of fully 2-D CNNs to separately exploit the collaborative spatial and spectral characteristics of HS and MS images and jointly predict the missing details of high-resolution HS image; (3) by adopting different sizes of image patches and numbers of convolutional layers, the structure features of HS and MS images within different scales can be automatically explored. Similar to the TCNNF method, the output of our network involves the details of each individual pixel, which avoids the potential errors that might be caused by overlaps of multiple patches. The remainder of this paper is organized as follows. Section 2 introduces the study datasets and elaborates the presented CNN-based detail injection method. Section 3 describes the experiments and results. Finally, the conclusion is drawn in Section 4.