In this paper, a new class of nonlinear thresholding functions with a tunable shape parameter for wavelet-based signal denoising is presented. In addition, a new learning technique for training of thresholding neural network is introduced. Unlike to existing methods, bo More
In this paper, a new class of nonlinear thresholding functions with a tunable shape parameter for wavelet-based signal denoising is presented. In addition, a new learning technique for training of thresholding neural network is introduced. Unlike to existing methods, both the shape and the threshold parameters are tuned simultaneously using LMS rule. This permits us to consider the effects of both the threshold and the shape parameters on denoising. The proposed functions are tested in both universal-threshold and subband-adaptive denoising and compared with conventional functions. In addition, to evaluate the proposed training method, several numerical examples are performed. The experimental results obtained from denoising of several standard benchmark signals confirm the efficiency and effectiveness of the proposed methods.
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In image inpainting, distorted and damaged parts of image or selected objects are removed or replaced with the appropriate information. In this article, image inpainting is performed by using frequency information of wavelet transform. The fill-in is done by diffusion o More
In image inpainting, distorted and damaged parts of image or selected objects are removed or replaced with the appropriate information. In this article, image inpainting is performed by using frequency information of wavelet transform. The fill-in is done by diffusion of information of intact pixels into the damaged regions, which is begun from the outermost pixels and gradually the damaged region is reconstructed. To determine direction and the amount of diffusion, the geodesic path based image inpainting method is generalized by incorporating information of wavelet domain. The experimental results confirm superiority of the proposed method over the geodesic path based image inpainting method.
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This article, an efficient system for texture defect detection based on curvelet transform is presented. The main idea is to model the defects in the texture image as one-dimensional discontinuities. Based on this idea, the curvelet transform is the most efficient meth More
This article, an efficient system for texture defect detection based on curvelet transform is presented. The main idea is to model the defects in the texture image as one-dimensional discontinuities. Based on this idea, the curvelet transform is the most efficient method for describing defects. First, in the learning phase, training samples of intact and defected blocks of the texture image are collected and transformed to the curvelet domain. Next, for each block a feature vector based on curvelet sub-bands is extracted and using a proposed method some important and effective features are determined for the desired texture. Then, a proper threshold for detecting defected from intact blocks is determined. In the performance phase, a vector containing the important features from each block of the texture is extracted and then the block by is classified. The results of simulation show that the proposed system is superior to the mean shift method in detecting defected texture blocks, and is less sensitive to the type of texture.
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