Poissonian Hyperspectral Image Superresolution Using Alternating Direction Optimization

The reconstruction of Poissonian image is an active research area in recent years. This paper proposes a novel method for Poissonian hyperspectral image superresolution by fusing a low-spatial-resolution hyperspectral image and a low-spectral-resolution multispectral image. The fusion scheme is designed as an optimization problem, whose cost function consists of the two data-fidelity terms about Poisson distribution, the sparse representation term, and the nonlocal regularization term. The two data-fidelity terms can capture statistical information of Poisson noise. The sparse representation term is used for enhancing the quality of sparsity-based signal reconstruction, and the nonlocal regularization term exploits the spatial similarity of hyperspectral image. As a result, the hyperspectral image and multispectral image are well fused. Finally, the designed optimization problem is effectively solved by an alternating direction optimization algorithm. Simulation results illustrate that the proposed method has a better performance than several well-known methods both in terms of quality indexes and reconstruction visual effect.