Hyper Spectral Imaging

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In this paper, a new algorithm for striping noise reduction in hyperspectral images is proposed. The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal. The algorithm does not introduce artifacts in the data and also takes into account the dependence on the signal intensity of the striping component. The effectiveness of the algorithm in reducing striping noise is experimentally demonstrated on real data acquired both by airborne and satellite hyperspectral sensors.

Existing System

The existing system available for fuzzy filters for noise reduction deals with fat-tailed noise like impulse noise and median filter.

  • Only Impulse noise reduction using fuzzy filters
  • Gaussian noise is not specially concentrated
  • It does not distinguish local variation due to noise and due to image structure.


Proposed System

The proposed system presents a new technique for filtering narrow-tailed and medium narrow-tailed noise by a fuzzy filter. The system,

  • First estimates a ?fuzzy derivative? in order to be less sensitive to local variations due to image structures such as edges
  • Second, the membership functions are adapted accordingly to the noise level to perform ?fuzzy smoothing.?
  • For each pixel that is processed, the first stage computes a fuzzy derivative. Second, a set of 16 fuzzy rules is fired to determine a correction term. These rules make use of the fuzzy derivative as input.
  • Fuzzy sets are employed to represent the properties, while the membership functions for and are fixed, the membership function for is adapted after each iteration.

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