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

Begovic, B. and Stankovic, V. and Stankovic, L.; (2011) Contrast enhancement and denoising of Poisson and Gaussian mixture noise for solar images. In: Proceedings of the 18th IEEE International Conference on Image Processing (ICIP). IEEE, BEL, pp. 185-188. ISBN 9781457713040

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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.

ORCID iDs

Begovic, B. ORCID logoORCID: https://orcid.org/0000-0003-0300-0922, Stankovic, V. ORCID logoORCID: https://orcid.org/0000-0002-1075-2420 and Stankovic, L. ORCID logoORCID: https://orcid.org/0000-0002-8112-1976;
  • Item type:Book Section
    ID code:42043
    Dates:
    Date
    Event
    September 2011
    Published
    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 , Electrical engineering. Electronics Nuclear engineering
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:14 Nov 2012 14:44
    Last modified:19 Mar 2021 04:27
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/42043
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