Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm

Research output: Contribution to journalConference articleScientificpeer-review


We consider hyperspectral phase/amplitude imaging from hyperspectral complex-valued noisy observations. Block-matching and grouping of similar patches are main instruments of the proposed algorithms. The search neighborhood for similar patches spans both the spectral and 2D spatial dimensions. SVD analysis of 3D grouped patches is used for design of adaptive nonlocal bases. Simulation experiments demonstrate high efficiency of developed state-of-the-art algorithms.

Original languageEnglish
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Issue number10
Publication statusPublished - 26 Jan 2020
Publication typeA1 Journal article-refereed
Event Image Processing: Algorithms and Systems Conference -
Duration: 26 Jan 202030 Jan 2020


  • Block matching
  • Complex domain
  • Denoising
  • Hyperspectral imaging
  • Sparsity

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Hyperspectral complex-domain image denoising: cube complex-domain BM3D (CCDBM3D) algorithm'. Together they form a unique fingerprint.

Cite this