Computational imaging with loraks: Reconstructing linearly predictable signals using low-rank matrix regularization
Although many modern computational imaging approaches use sparse or low-rank modeling constraints, it should not be forgotten that many classical computational imaging approaches rely on a different kind of constraint: linear predictability. Several years ago, we introduced the LORAKS framework, which combines classical linear prediction models with modern low-rank matrix completion approaches. LORAKS makes the observation that linear prediction assumptions can be embedded into high-dimensional structured low-rank matrix constraints. This enables a powerful and flexible new approach for modeling and reconstructing images. In this work, we review LORAKS and describe our recent progress.