Author(s): Srinivasareddy Putluri
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Vol.1 - Issue 7 (September - 2012)
reconstruct 3D sparse images using Non-linear Filtering Compressed Sensing (NFCS) 3D Anisotropic and 3D Isotropic techniques. Compressed Sensing is a new paradigm for signal recovery and sampling. Using proposed NFCS techniques, a random mask is generated for the selected input 3D image using a sampling matrix. Now, a sparse image is generated for the selected 3D image. Applying Normalization, the original image will be reconstructed from the sparse 3D image in iterations. The obtained reconstructed 3D image will be filtered using an appropriate adaptive non-linear filter in proposed NFCS 3D techniques. The output parameters like Normalized Factor, PSNR, and CPU elapsed time will be calculated and compared along with simulation results. The results obtained using proposed techniques will confirm that these possess properties of efficiency, stability, low computational cost, fast recovery of 3D images in few seconds and its performance is competitive with those of state of art algorithms.
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