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Deep sparse representation driven network for compressive imaging
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AI
DOI：10.1016/j.knosys.2025.114577.png)
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Abstract
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• We propose a novel deep sparse representation model-driven network termed DeSRNet. • The local and global features are explicitly exploited to generate adaptive thresholds. • The proposed unrolled iterative method termed DUN-DeSRNet can achieve high-quality reconstructions in CI tasks. • We prove that DUN-DeSRNet can generate fixed-point convergent trajectories.
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