SLIC super-pixels for multi-resolution compressive spectral imaging reconstruction

H. García, C. V. Correa, H. Argüello

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Base Information

Volume

V51 - N3 / 2018 Ordinario

Reference

50304:1-10

DOI

http://doi.org/10.7149/OPA.51.3.50304

Language

English

Keywords

Multi-resolution, super-pixels, single pixel camera, compressive spectral imaging.

Abstract

Spectral imaging (SI) is widely used in different applications involving material identification since it contains both spatial (x,y) and spectral information (λ). However, traditional SI scanning methods involve massive amounts of data, which increase the cost of storing and processing. Compressive sensing (CS) theory has been applied in SI, such that the underlying data cube can be recovered from a reduced number of measures. Reconstructions are obtained by l₂-l₁ norm-based algorithms whose computational complexity grows in proportion to the number of unknowns. In this paper, a multi-resolution reconstruction model based on the simple linear iterative clustering (SLIC) is proposed to reduce the number of unknown values to recover. Simulation results show that the proposed method is up to 86% faster than the full-resolution reconstructions. Additionally, MR approach obtains more accurate reconstructions with improvements of up to 12dB of PSNR.

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