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BPDQp-Net: A Deep Unfolding Method forQuantized Compressed Sensing
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DOI：10.1109/LSP.2024.3356417.png)
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摘要
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As a classic quantization compressed sensing method, the Basis Pursuit DeQuantizer of moment p (BPDQ(p)) theoretically shows that compared to l(2) norm (p > 2) can more faithfully model quantization distortion and its performance improves with larger p. However, as the p increases, the computational complexity of the algorithm increases sharply, which undoubtedly greatly limits the application of BPDQ(p) in practical scenarios. Moreover, limited by manual parameter fine-tuning, the performance of the algorithm is also difficult to fully release. To solve these problems, we design a modified BPDQ(p) , and by mapping its solving process into a neural network structure, we further propose a deep unfolding model, named BPDQ(p) -Net, whose network architecture does not change with p. Since all model parameters can be optimized and updated with training data, the proposed model can accurately and efficiently reconstruct quantization signals. Our simulation results show that compared with BPDQ(p) , the proposed BPDQ(p) -Net improves the signal reconstruction speed by three orders of magnitude and the reconstruction accuracy by about 50%.
Keyword:
Compressed sensing
Transforms
Training data
deep unfolding
quantized measurements
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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