Learning Spectral-wise Correlation for Spectral Super-Resolution: Where Similarity Meets Particularity

Hongyuan Wang, Lizhi Wang*, Chang Chen, Xue Hu, Fenglong Song, Hua Huang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hyperspectral images consist of multiple spectral channels, and the task of spectral super-resolution is to reconstruct hyperspectral images from 3-channel RGB images, where modeling spectral-wise correlation is of great importance. Based on the analysis of the physical process of this task, we distinguish the spectral-wise correlation into two aspects: similarity and particularity. The Existing Transformer model cannot accurately capture spectral-wise similarity due to the inappropriate spectral-wise fully connected linear mapping acting on input spectral feature maps, which results in spectral feature maps mixing. Moreover, the token normalization operation in the existing Transformer model also results in its inability to capture spectral-wise particularity and thus fails to extract key spectral feature maps. To address these issues, we propose a novel Hybrid Spectral-wise Attention Transformer (HySAT). The key module of HySAT is Plausible Spectral-wise self-Attention (PSA), which can simultaneously model spectral-wise similarity and particularity. Specifically, we propose a Token Independent Mapping (TIM) mechanism to reasonably model spectral-wise similarity, where a linear mapping shared by spectral feature maps is applied on input spectral feature maps. Moreover, we propose a Spectral-wise Re-Calibration (SRC) mechanism to model spectral-wise particularity and effectively capture significant spectral feature maps. Experimental results show that our method achieves state-of-the-art performance in the field of spectral super-resolution with the lowest error and computational costs.

Original languageEnglish
Title of host publicationMM 2023 - Proceedings of the 31st ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages7676-7685
Number of pages10
ISBN (Electronic)9798400701085
DOIs
Publication statusPublished - 26 Oct 2023
Event31st ACM International Conference on Multimedia, MM 2023 - Ottawa, Canada
Duration: 29 Oct 20233 Nov 2023

Publication series

NameMM 2023 - Proceedings of the 31st ACM International Conference on Multimedia

Conference

Conference31st ACM International Conference on Multimedia, MM 2023
Country/TerritoryCanada
CityOttawa
Period29/10/233/11/23

Keywords

  • spectral super-resolution
  • spectral-wise particularity
  • spectral-wise similarity
  • transformer

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