Generalized k-level cutset sampling and reconstruction

Abstract

We propose a family of cutset sampling schemes and a generalized k-level image reconstruction approach formulated under a minimum mean squared error (MMSE) framework. The k-level reconstruction approach is a direct generalization of the recently proposed pattern-based approach, and can be applied to periodic samples either on a cutset or on a grid. Our experimental results indicate that the generalization of the k-level reconstruction approach results in only a small performance loss. For rectangular cutsets, we show that the proposed approach outperforms the cutset-MRF approach as well as two inpainting approaches. Moreover, we show that combining the cutset sampling with an additional point sample inside the periodic structure outperforms k-level reconstruction from cutset sampling and point sampling under comparable sampling densities.