Joint Sparse Representation-based Single Image Super-Resolution for Remote Sensing Applications

Abstract

Sparse representation-based single image super-resolution (SISR) methods use a coupled overcomplete dictionary trained from high-resolution (HR) images/ image patches. Since, remote sensing (RS) satellites capture images of large areas, these images usually have poor spatial resolution and obtaining an effective dictionary as such would be very challenging. Moreover, traditional patch-based sparse representation (PSR) models for reconstruction tend to give unstable sparse solution and produce visual artefact in the recovered images. To mitigate these problems, in this paper, we have proposed an adaptive joint sparse representation (JSR)-based SISR method that is dependent only on the input low-resolution (LR) image for dictionary training and sparse reconstruction. The new model combines patch-based local sparsity and group sparse representation (GSR)-based non-local sparsity in a single framework, which helps in stabilizing the sparse solution and improve the SISR results. Experimental results are evaluated both visually and quantitatively for several RGB and multispectral RS datasets, where the proposed method shows improvements in PSNR by 1–4 dB and 2–3 dB over the state-of-the-art sparse representation- and deep learning-based SR methods, respectively. Land cover classification applied on the super-resolved images further validate the advantages of the proposed method. Finally, for practical RS applications, we have performed parallel implementation in general purpose graphics processing units (GPGPU) and achieved significant speed-ups (30-40×) in the execution time.