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- W2597657496 abstract "High reconstructed performance compressed video sensing (CVS) with low computational complexity and memory requirement is very challenging. In order to reconstruct the high quality video frames with low computational complexity, this paper proposes a tensor-based joint sparseness regularization CVS reconstruction model FrTVCST (fractional-order total variation combined with sparsifying transform), in which a high-order tensor fractional-order total variation (FrTV) regularization and a tensor discrete wavelet transform (DWT) L0 norm regularization are combined. Furthermore, an approach for choosing the regularization parameter that controls the influence of the two terms in this joint model is proposed. Afterwards, a tensor gradient projection algorithm extended from smoothed L0 (SL0) is deduced to solve this combined tensor FrTV and DWT joint regularization constrained minimization problem, using a smooth approximation of the L0 norm. Compared with several state-of-the-art CVS reconstruction algorithms, such as the Kronecker compressive sensing (KCS), generalized tensor compressive sensing (GTCS), N-way block orthogonal matching pursuit (N-BOMP), low-rank tensor compressive sensing (LRTCS), extensive experiments with commonly used video data sets show the competitive performance of the proposed algorithm with respect to the peak signal-to-noise ratio (PSNR) and subjective visual quality. A FrTV combined with tensor DWT for CS video reconstruction model is proposed.A method for estimating the regularization parameter is proposed.A tensor smoothed L0 algorithm is developed to solve this reconstruction model.The algorithm has a higher PSNR and better detail preservation." @default.
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- W2597657496 date "2017-07-01" @default.
- W2597657496 modified "2023-10-03" @default.
- W2597657496 title "Tensor compressed video sensing reconstruction by combination of fractional-order total variation and sparsifying transform" @default.
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- W2597657496 doi "https://doi.org/10.1016/j.image.2017.03.021" @default.
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