Image denoising in wavelet domain using a new thresholding function
Information Science and Technology（2011）
摘要
Improving quality of noisy images has been an active area of research in many years. It has been shown that wavelet thresholding methods had better results than classic approaches. However estimation of threshold and selection of thresholding function are still the challenging tasks. In this paper, a new thresholding function is proposed for wavelet thresholding. This function is continues and has higher order derivation. Therefore it is suitable for gradient decent learning methods such as thresholding neural network (TNN). This function is used by the TNN and threshold values for wavelet sub-bands are estimated according to least mean square (LMS) algorithm. The experimental results show improvement in noise reduction from images based on visual assessments and PSNR comparing with well-known thresholding functions.
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关键词
image denoising,image segmentation,least mean squares methods,neural nets,wavelet transforms,psnr,gradient decent learning,least mean square algorithm,thresholding neural network,visual assessment,wavelet subband,wavelet thresholding method,thresholding function,wavelet thresholding,higher order,lms algorithm,least mean square,noise measurement,artificial neural networks,neural network,artificial neural network,noise reduction,noise,functional imaging
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