Contrast enhancement and denoising of Poisson and Gaussian mixture noise for solar images

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

6 Citations (Scopus)

Abstract

Processing of solar image data has become increasingly important for accurate space weather prediction and expanding our understanding about the Sun and Universe. To enable proper analysis, image denoising and contrast enhancement are essential for removal of all artifacts introduced within the acquisition process. Hence, this paper focuses on these two tasks applied on solar images corrupted with pixel dependent Poisson and zero-mean additive Gaussian noise. The denoising frameworks are build upon on two state-of-the-art techniques, K-SVD and BM3D (for natural images) where contrast enhancement of noisy solar images is performed jointly with noise removal using sparse coding adaptive dictionary learning. Results are given for two conventional sets of solar images.

Original languageEnglish
Title of host publicationProceedings of the 18th IEEE International Conference on Image Processing (ICIP)
Place of PublicationNew York
PublisherIEEE
Pages185-188
Number of pages4
ISBN (Print)9781457713040
DOIs
Publication statusPublished - Sep 2011
EventICIP 2011: 2011 IEEE International Conference on Image Processing - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011

Conference

ConferenceICIP 2011: 2011 IEEE International Conference on Image Processing
CountryBelgium
CityBrussels
Period11/09/1114/09/11

Keywords

  • representation
  • dictionaries
  • sparse
  • sparse coding
  • curvelet transform
  • image enhancement
  • contrast enhancement
  • solar images
  • denoising
  • dictionaries
  • transforms
  • noise reduction
  • noise measurement
  • image denoising
  • gaussian noise

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