Super-Resolution of remotely sensed images using SRVPLR and SRASW

In this paper, we present the current implementation and applications of two super-resolution algorithms, called Super-Resolution Variable-Pixel Linear Reconstruction (SRVPLR) and Super-Resolution with Additive-Substitutive Wavelets (SRASW). Each of them combines a set of undersampled lower resolution images in order to obtain a super- resolution product with a significantly improved limiting spatial resolution. We show their current implementation for remote sensing and astronomical images with some applications to several data sets. These examples show that the algorithms provide substantial improvement in limiting spatial resolution for both simulated and real data sets without significantly altering the multispectral content of the input low-resolution images, without amplifying the noise, and with very few artifacts.