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- W2969969795 abstract "Block-based compressed sensing (CS) recovery aims to reconstruct the high quality image from only a small number of observations in a block-wise manner. However, when the sampling rate is very low and the existence of additive noise, there are usually some block artifacts and detail blurs which degrades the reconstructed quality. In this paper, we propose an efficient method which takes both the advantages of deep learning (DL) framework and iterative approaches. First, a deep multi-layer perceptron (DMLP) is constructed to obtain the initial reconstructed image. Then, an efficient iterative approach is applied to keep the consistence and smoothness between the adjacent blocks. The proposed method demonstrates its efficacy on benchmark datasets." @default.
- W2969969795 created "2019-08-29" @default.
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- W2969969795 date "2019-01-01" @default.
- W2969969795 modified "2023-10-18" @default.
- W2969969795 title "Low-Sampling Imagery Data Recovery by Deep Learning Inference and Iterative Approach" @default.
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- W2969969795 doi "https://doi.org/10.1007/978-3-030-29551-6_44" @default.
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