International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.8, No.10, Oct. 2016
Compressive Sensing based Image Reconstruction Using Generalized Adaptive OMP with Forward-Backward Movement
Full Text (PDF, 787KB), PP.19-28
Reconstruction of a sparse signal from fewer observations require compressive sensing based recovery algorithm for saving memory storage. Various sparse recovery techniques including l_1 minimization, greedy pursuit approaches and non-convex optimization requires sparsity to be known in advance. This article presents the generalized adaptive orthogonal matching pursuit with forward-backward movement under the cumulative coherence property; which removes the need of knowledge of sparsity prior to implementation. In this technique, the forward step increases the size of support set and backward step eliminates the misidentified elements. It selects multiple indices on the basis of maximum correlation by forward-backward movement. The size of backward step is kept smaller than the forward one. These forward-backward steps then iterate and increment through the algorithm adaptively and terminate with stopping condition to ensure the identification of significant components. Recovery performance of proposed algorithm is demonstrated via simulation results including reconstruction of sparse signals in noisy and noise free environment. The algorithm has major advantage that it does not require the knowledge of sparsity in advance in contrast to the earlier reconstruction techniques. The evaluation and comparative analysis of result shows that algorithm leads to the increment in recovery performance and efficiency considerably.
Cite This Paper
Meenakshi, Sumit Budhiraja,"Compressive Sensing based Image Reconstruction Using Generalized Adaptive OMP with Forward-Backward Movement", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.10, pp.19-28, 2016.DOI: 10.5815/ijigsp.2016.10.03
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