Volume : V, Issue : IX, September - 2016

NOISE REDUCTION IN SPARSE RECONSTRUCTED IMAGES

Jeena Mary Roy, Sabarinath. G

Abstract :

 Compressive Sensing theory states that accurate reconstruction of the sparse signal is possible even from a sampling rate dramatically smaller than the Nyquist rate. For sparse signal reconstruction three techniques are commonly used: convex relaxation, greedy pursuit and probabilistic method. In Sparse signal reconstruction using GEM hard thresholding the method used for reconstruction is probabilistic model with ?0 – norm and an algorithm is developed known as GEM algorithm. The drawback of GEM method is that quality of reconstructed image is inadequate for further processing . To improve the quality of image, Iterative Multiplier noise reduction algorithm is tested on GEM reconstructed sparse image. Numerical evaluation results are reported.

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Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

Jeena Mary Roy, Sabarinath. G NOISE REDUCTION IN SPARSE RECONSTRUCTED IMAGES International Journal of Scientific Research,Volume : 5 | Issue : 9 |September 2016

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