Although sparsecoding error has been introduced to improve the performance of sparse representation-based image denoising, however, the sparsecoding noise is not tight enough. To suppress the sparsecoding noise, we...
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ISBN:
(纸本)9783319736006;9783319735993
Although sparsecoding error has been introduced to improve the performance of sparse representation-based image denoising, however, the sparsecoding noise is not tight enough. To suppress the sparsecoding noise, we exploit a couple of images to estimate unknown sparse code. There are two main contributions in this paper: The first is to use a reference denoised image and an intermediate denoised image to estimate the sparse coding coefficients of the original image. The second is that we set a threshold to rule out blocks of low similarity to improve the accuracy of estimation. Our experimental results have shown improvements over several state-of-the-art denoising methods on a collection of 12 generic natural images.
Optimal random network coding is reduced complexity in computation of codingcoefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coef...
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Optimal random network coding is reduced complexity in computation of codingcoefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in codingcoefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in codingcoefficients resulting lower triangular codingcoefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix.
Various image priors, such as sparsity prior, non-local self-similarity prior and gradient histogram prior, have been widely used for noise removal, while preserving the image texture. However, the gradient histogram ...
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Various image priors, such as sparsity prior, non-local self-similarity prior and gradient histogram prior, have been widely used for noise removal, while preserving the image texture. However, the gradient histogram prior used for texture enhancement sometimes generates false textures in the smooth areas. In order to address these problems, the authors propose a robust algorithm combining gradient histogram with sparse representation to obtain good estimates of the sparse coding coefficients of the latent image and realising image denoising while preserving the texture. The proposed model is solved by having a balance between over-enhancement and over-smoothing of the texture in order to preserve the natural texture appearance. Experimental results demonstrate the efficiency and effectiveness of the proposed method.
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