A non-negative sparse promoting algorithm for high resolution hyperspectral imaging

Promoting the spatial resolution of off-the-shelf hyperspectral sensors is expected to improve typical computer vision tasks, such as target tracking and image classification. In this paper, we investigate the scenario in which two cameras, one with a conventional RGB sensor and the other with a hyperspectral sensor, capture the same scene, attempting to extract redundant and complementary information. We propose a non-negative sparse promoting framework to integrate the hyperspectral and RGB data into a high resolution hyperspectral set of data. The formulated problem is in the form of a sparse non-negative matrix factorization with prior knowledge on the spectral and spatial transform responses, and it can be handled by alternating optimization where each subproblem is solved by efficient convex optimization solvers; e.g., the alternating direction method of multipliers. Experiments on a public database show that our method achieves much lower average reconstruction errors than other state-of-the-art methods.