Spatial interpolation methods for spectral-spatial remote sensing image super-resolution algorithm based on gradient descent approach

Abstract

The paper presents an investigation of the influence of spatial interpolation methods on the quality of the image obtained as a result of the spectral-spatial remote sensing image super-resolution reconstruction based on the gradient descent approach. As an example of the super-resolution method, we applied our earlier developed super-resolution image reconstruction algorithm. The algorithm provides the minimization of error of the observation model that connects the input low-resolution images with the target high-resolution image. The iterations of the gradient descent method are performed in the high-resolution spectral and spatial coordinates grid. For this reason, the spatial interpolation operator is added in the observation model. It is evident that spatial interpolation affects both the quality of the reconstructed image and the algorithm convergence rate. The objective of our research was to define the most appropriate spatial interpolation method. The paper presents the results of the spectral-spatial super-resolution image reconstruction using the following spatial interpolation methods: bilinear, bicubic, sinc, and nearest neighbour interpolation. We compare these implementations in terms of such image quality indicators as the root mean square error of the estimated high-resolution image, the algorithm convergence rate, and the presence of textural and colour artefacts as well.