Deep convolutional neural network for single remote sensing image super resolution

The existing deep learning-based remote sensing image super-resolution (SR) reconstruction methods have some problems, such as insufficient feature extraction and detail loss. We propose an efficient and fast deep convolutional neural network (CNN) single-image SR model, which achieves relatively advanced reconstruction performance. The number of layers and filter layers of CNNs are completely optimized, and the computing cost is significantly reduced. The global and local features of the original image are extracted by multiscale features; then, contextual information is enhanced in the inference network, and the reconstruction is done in advance of the dimensionality reduction operation to reduce the computational effort. The reconstruction performance is further improved using skip connection network in the whole network to solve the inefficiency of information in the transmission process. The experimental results show that the algorithm is 0.32 to 4.38 dB higher than other algorithms in peak signal-to-noise ratio value, the structure similarity index measure value reaches 0.9618, and the reconstructed image extracts more features and is richer in details.