An Efficient Image Reconstruction Technique with Aid of PSO (Particle Swarm Optimization) and DWT (Discrete Wavelet Transform)
(*) Corresponding author
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Image Reconstruction is to retrieve the original image (or a general signal) from its given awful version, for e.g., an image that is corrupted by noise, blurred by atmospheric turbulence (as in certain astronomic observations), or that has some scratched regions. Different reconstruction methods were utilized for performing the image reconstruction process. In such works, there is a lack of analysis in considering the reconstructed image quality because the reconstructed image seems to be blurred and poor in quality and so yielded less accuracy in the image reconstruction process. So avoid such drawbacks in the existing methods a new image reconstruction technique is proposed in this paper. The proposed technique comprised of two major phases (i) training phase (ii) investigation phase. In training phase, the given cracked image is reconstructed by the DWT (Discrete wavelet Transform) method by selecting optimal threshold value using PSO (Particle Swarm Optimization). These selected threshold values are exploited in the image reconstruction process. In investigation phase, the threshold value is selected based on the crack level of the testing image. By combining the DWT and PSO optimization in the proposed technique, the reconstructed image is obtained with high quality. The implementation result shows the effectiveness of proposed image reconstruction technique in reconstruct the image with different crack variance. The performance of the image reconstruction technique is evaluated by comparing the result of proposed technique with the average filtering image reconstruction technique. The comparison result shows a high-quality reconstructed image for the noisy images than the existing method, in terms of peak signal-to-noise ratio (PSNR).
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Carsten Denker, Alexandra Tritschler and Mats Lofdahl, "Image Reconstruction", Encyclopedia of Optical Engineering, New York, 2004
Puetter, Gosnell and Amos Yahil, "Digital Image Reconstruction: Deblurring and Denoising", Annual Review of Astronomy & Astrophysics, Vol. 43, No. 1, pp.139-194, 2005
Adam Alessio and Paul Kinahan, "PET Image Reconstruction", Second Edition Nuclear Medicine, Elsevier; pp. 1-22, 2006
Michael Liebling, "Robust Multiresolution Techniques for Image Reconstruction", In Proceedings of Conference on SPIE, Vol. 6437, pp. 64371C-1-64371C-4, 2007
Joachim Dahl, Per Christian Hansen, Soren Holdt Jensen and Tobias Lindstrøm Jensen, "Algorithms and software for total variation image reconstruction via ﬁrst-order methods", Numerical Algorithms, Vol. 53, pp. 67-92, 2010
Feng Yu, "Statistical Methods for Transmission Image Reconstruction with Nonlocal Edge-Preserving Regularization", Thesis, 2000
Jeffrey A. Fessler and Leslie Rogers, "Resolution Properties of Regularized Image Reconstruction Methods", Technical Report No. 297, 1995
Martin Schweiger, Simon R Arridge and Ilkka Nissila, "Gauss–Newton method for image reconstruction in diffuse optical tomography", Physics in Medicine and Biology, Vol. 50, pp. 2365-2386, 2005
Sangtae Ahn, "Convergent Algorithms for Statistical Image Reconstruction in Emission Tomography", Thesis, 2004
Schweiger and Arridge, "Optical tomographic reconstruction in a complex head model using a priori region boundary information", Physics in Medicine and Biology, Vol. 44, pp. 2703-2721, 1999
Hiltunen, Prince and Arridge, "A combined reconstruction–classiﬁcation method for diffuse optical tomography", Physics in Medicine and Biology, Vol. 54, pp. 6457-6476, 2009
Laura B. Montefusco, Damiana Lazzaro, Serena Papi and Carla Guerrini, "A Fast Compressed Sensing Approach to 3D MR Image Reconstruction", IEEE Transactions on Medical Imaging, Vol. 30, No. 5, pp. 1064-1075, 2011
Brent A. Williams and David G. Long, "Reconstruction from Aperture-Filtered Samples With Application to Scatterometer Image Reconstruction", IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 5, pp. 1663-1676, 2011
Hakan Erturk, "Evaluation of image reconstruction algorithms for non-destructive characterization of thermal interfaces", International Journal of Thermal Sciences, Vol. 50, pp. 906-917, 2011.
Ravi Saharan and Choudhary Vijaypal Singh, "Reassembly of 2D Fragments in Image Reconstruction", International Journal of Computer Applications, Vol. 19, No.5, pp. 41-45, 2011.
Peyman Rahmati, Manuchehr Soleimani, Sven Pulletz, Inez Frerichs and Andy Adler, "Level Set based Reconstruction Algorithm for EIT Lung Images: First Clinical Results", Physiological Measurement, Vol. 33, No. 5, pp. 1-14, 2012.
Kanakaraj and Kathiravan, "Super-resolution image reconstruction using sparse parameter dictionary framework", Scientific Research and Essays, Vol. 7, No. 5, pp. 586-592, 2012.
Chen, Wei-neng and Zhang, Jun, "A novel set-based particle swarm optimization method for discrete optimization problem". IEEE Transactions on Evolutionary Computation, Vol. 14, No.2, pp. 278–300, 2010.
- There are currently no refbacks.
Please send any question about this web site to firstname.lastname@example.org
Copyright © 2005-2022 Praise Worthy Prize