Digital image filtering in wavelet domain using genetic programming

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).        
 

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