RPFNET: Complementary Feature Fusion For Hand Gesture Recognition

Hyperspectral super-resolution based on coupled Tucker decomposition has been recently considered in the remote sensing community. The state-of-the-art approaches did not fully exploit the coupling information contained in hyperspectral and multispectral images of the same scene. in this paper, we propose a new algorithm that overcomes this limitation. It accounts for both the high-resolution and the low-resolution information in the model, by solving a set of least squares problems. in addition, we provide exact recovery conditions for the super-resolution image in the noiseless case. Our simulations show that the proposed algorithm achieves good reconstruction with low complexity.