Remote sensing image super-resolution reconstruction using depth-dense residual and sub-pixel convolutional model

Existing super-resolution reconstruction algorithms for remote sensing images often struggle to fully extract and utilize features in complex scenes, and the reconstruction results are not optimal due to the influence of noise. We propose a reconstruction network model that combines deep dense residual module and sub-pixel convolution. This model connects multiple dense residual modules through recursive linking and introduces a channel attention mechanism to extract multiscale features from the images. During the feature reconstruction process, a sub-pixel convolution structure is introduced to reduce the impact of noise and enhance reconstruction performance. The model is tested on the UC Merced Land Use public dataset, and the results demonstrate that the reconstruction results of our proposed model algorithm have better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at different scales compared to the current mainstream reconstruction algorithm EDSR. It is proven that the proposed model can significantly improve the reconstruction quality through comparative analysis experiment, meeting the needs for high-resolution remote sensing image processing.