Hyperspectral Image Restoration Using Low-Rank Matrix Recovery

Hyperspectral images (HSIs) are often degraded by a mixture of various kinds of noise in the acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes, and so on. This paper introduces a new HSI restoration method based on low-rank matrix recovery (LRMR), which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. By lexicographically ordering a patch of the HSI into a 2-D matrix, the low-rank property of the hyperspectral imagery is explored, which suggests that a clean HSI patch can be regarded as a low-rank matrix. We then formulate the HSI restoration problem into an LRMR framework. To further remove the mixed noise, the “Go Decomposition”…

• PU
  Purdue University
• NN
  National Natural Science Foundation of China

  Awards: 61201342, 40930532
• NH
  National High-tech Research and Development Program

  Award: 2013AA12A301
• NK
  National Key Research and Development Program of China

  Award: 2011CB707105