QIS-GAN: A lightweight adversarial network with quadtree implicit sampling for multispectral and hyperspectral image fusion

Shangqi Deng

Abstract: Multispectral and hyperspectral image fusion (MHIF) involves the fusion of high-spatial-resolution multispectral images (HR-MSIs) and low-spatial-resolution hyperspectral images (LR-HSIs) to generate high-spatial-resolution hyperspectral images (HR-HSIs) and has gained significant attention in the field of remote-sensing imaging. While CNN and Transformer models have shown effectiveness in MHIF, existing CNN- or Transformer-based algorithms are overburdened with model size, making it difficult to achieve an effective tradeoff between fusion accuracy and degree of lightweight. Recently, implicit neural representation (INR) has been proven good interpretability and the ability to exploit coordinate information in 2-D tasks.