Sparse Representation of 3D Images for Piecewise Dimensionality Reduction with High Quality Reconstruction

Laura Rebollo-Neira, Daniel Whitehouse

Research output: Contribution to journalArticle

Abstract

Sparse representation of 3D images is considered within the context of data reduction. The goal is to produce high quality approximations of 3D images using fewer elementary components than the number of intensity points in the 3D array. This is achieved by means of a highly redundant dictionary and a dedicated pursuit strategy especially designed for low memory requirements. The benefit of the proposed framework is illustrated in the first instance by demonstrating the gain in dimensionality reduction obtained when approximating true color images as very thin 3D arrays, instead of performing an independent channel by channel approximation. The full power of the approach is further exemplified by producing high quality approximations of hyper-spectral images with a reduction of up to 371 times the number of data points in the representation.
Original languageEnglish
JournalArray
Early online date27 Jun 2019
DOIs
Publication statusE-pub ahead of print - 27 Jun 2019

Bibliographical note

Creative Commons Attribution 4.0 International (CC BY 4.0)

Fingerprint Dive into the research topics of 'Sparse Representation of 3D Images for Piecewise Dimensionality Reduction with High Quality Reconstruction'. Together they form a unique fingerprint.

  • Cite this