A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity
Laurent Jacques, Laurent Duval, Caroline Chaux, Gabriel Peyr\'e

TL;DR
This paper reviews recent advances in multiscale, multi-orientation image representations that balance efficiency and complexity, highlighting their geometric properties, invariance, and extensions to non-Euclidean domains.
Contribution
It provides a comprehensive overview of multiscale geometric decompositions, discussing their properties, algorithms, and extensions, offering insights into current research directions.
Findings
Multiscale, multi-orientation dictionaries improve sparsity and invariance in image representations.
Redundant dictionaries require specialized algorithms for efficient sparse coding.
Extensions to non-Euclidean domains expand the applicability of geometric decompositions.
Abstract
The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours and textures. The most recent ones, proposed in the past decade, share an hybrid heritage highlighting the multiscale and oriented nature of edges and patterns in images. This paper presents a panorama of the aforementioned literature on decompositions in multiscale, multi-orientation bases or dictionaries. They typically exhibit redundancy to improve sparsity in the transformed domain and sometimes its invariance with respect to simple geometric deformations (translation, rotation). Oriented multiscale dictionaries extend traditional wavelet…
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