Digital image filtering in wavelet domain using genetic programming
Editorial
10.22099/ijste.2006.876
Abstract
Genetic Programming (GP) is a powerful machine learning technique derived from genetic algorithms. We used GP to generate a mathematical function for image denoising based on statistical features derived from detail sub-bands of wavelet transform (WT). The function obtained from GP for image denoising is not dependent to any parameters as represented in other image denoising methods based on WT. Results of the proposed image denoising method is compared to the VisuShrink soft threshold image denoising method, both perceptually and in terms of Peak Signal to Noise Ratio (PSNR).
(2006). Digital image filtering in wavelet domain using genetic programming. Iranian Journal of Science and Technology Transactions of Electrical Engineering, 30(6), 701-710. doi: 10.22099/ijste.2006.876
MLA
. "Digital image filtering in wavelet domain using genetic programming", Iranian Journal of Science and Technology Transactions of Electrical Engineering, 30, 6, 2006, 701-710. doi: 10.22099/ijste.2006.876
HARVARD
(2006). 'Digital image filtering in wavelet domain using genetic programming', Iranian Journal of Science and Technology Transactions of Electrical Engineering, 30(6), pp. 701-710. doi: 10.22099/ijste.2006.876
VANCOUVER
Digital image filtering in wavelet domain using genetic programming. Iranian Journal of Science and Technology Transactions of Electrical Engineering, 2006; 30(6): 701-710. doi: 10.22099/ijste.2006.876
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