Multi-Image Super-Resolution Using Graph Neural Networks

This chapter investigates the potential of graph neural networks (GNNs) in multi-image super-resolution (MISR), positioning them against current state-of-the-art techniques. It introduces foundational concepts of super-resolution reconstruction (SRR), with a focus on the distinctions between single-image super-resolution (SISR) and MISR, and reviews key models in the field. Theoretical underpinnings of graph theory relevant to SRR are explored, alongside detailed descriptions of the architecture and data preparation methodologies for two GNN models designed specifically for MISR tasks. An evaluation conducted using benchmark datasets assesses these models through a training procedure and a comprehensive set of performance metrics. Findings reveal both the strengths and limitations of GNNs in enhancing image resolution, showcasing their effectiveness, and identifying areas where improvements are needed compared to leading methods. The chapter concludes with a discussion of the implications of these results, highlighting the promising yet challenging role of GNNs in MISR and suggesting directions for future research to advance this field.