Multiscale Sparse Image Representation with Learned Dictionaries (PREPRINT).

摘要

This paper introduces a new framework for learning multiscale sparse representations of natural images with overcomplete dictionaries. Our work extends the K-SVD algorithm (1), which learns sparse single-scale dictionaries for natural images. Recent work has shown that the K-SVD can lead to state-of- the-art image restoration results 2, (3). We show that these are further improved with a multiscale approach, based on a Quadtree decomposition. Our framework provides an alternative to multiscale pre-defined dictionaries such as wavelets, curvelets, and contourlets, with dictionaries optimized for the data and application instead of pre-modelled ones.