Image denosing based on wavelet support vector regression

Denoising is an important application of image processing. We have constructed a denoising system which learns an optimal mapping from the input data to denoised data. The Morlet wavelet was used as the kernel function to construct the wavelet support vector machine. The noised image data is mapped to denoised values by wavelet support vector regression. The result shows that denoising via wavelet support vector regression could perform better than Gaussian smoothing, median filtering and average filtering on the experimental image and it also performs better than Gaussian radial basic function support vector regression.