Cross-scale predictive dictionaries
This addresses efficiency bottlenecks in sparse representation methods for researchers and practitioners in computer vision and signal processing, though it is incremental as it builds on existing dictionary-based models.
The paper tackles the computational expense of solving inverse problems with sparsifying dictionaries by incorporating a multi-scale predictive structure, achieving speedups of 10-60× with minimal accuracy loss for visual signals like images and videos.
Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations. However, solving inverse problems with sparsifying dictionaries can be computationally expensive, especially when the dictionary under consideration has a large number of atoms. In this paper, we incorporate additional structure on to dictionary-based sparse representations for visual signals to enable speedups when solving sparse approximation problems. The specific structure that we endow onto sparse models is that of a multi-scale modeling where the sparse representation at each scale is constrained by the sparse representation at coarser scales. We show that this cross-scale predictive model delivers significant speedups, often in the range of 10-60$\times$, with little loss in accuracy for linear inverse problems associated with images, videos, and light fields.