Spiralet Sparse Representation
This addresses a limitation in sparse representation methods for fields like image processing, though it appears incremental as it builds on known sparse representation capabilities.
The paper tackles the problem of preserving spatial relations in sparse representations of multi-dimensional data like images, proposing a new approach called spiralet sparse representation to augment and modify existing sparse representation theory.
This is the first report on Working Paper WP-RFM-14-01. The potential and capability of sparse representations is well-known. However, their (multivariate variable) vectorial form, which is completely fine in many fields and disciplines, results in removal and filtering of important "spatial" relations that are implicitly carried by two-dimensional [or multi-dimensional] objects, such as images. In this paper, a new approach, called spiralet sparse representation, is proposed in order to develop an augmented representation and therefore a modified sparse representation and theory, which is capable to preserve the data associated to the spatial relations.