SURE-TISTA: A Signal Recovery Network for Compressed Sensing
| Autor: | Liang Wu, Mengcheng Yao, Zaichen Zhang, Jian Dang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Minimum mean square error
Artificial neural network Computer science business.industry Deep learning Estimator 020206 networking & telecommunications 02 engineering and technology 010501 environmental sciences Inverse problem 01 natural sciences Compressed sensing Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Algorithm 0105 earth and related environmental sciences |
| Zdroj: | ICASSP |
| Popis: | Deep neural network (DNN) has a wide range of applications in various fields, including solving sparse inverse problems. In this paper, we propose a novel network called the Stein’s unbiased risk estimate based-trainable iterative thresholding algorithm (SURE-TISTA) for sparse signal recovery problems. Without prior information, SURE-TISTA outperforms TISTA, an algorithm based on the minimum mean squared error (MMSE) estimator. SURE-TISTA also shows a great robustness in many cases including large-scale and large-variance problems. Meanwhile, SURE-TISTA uses fewer learnable variables to achieve similar performance as learned approximate message passing (LAMP), which has more learnable parameters. Without any error measure estimator, SURE-TISTA achieves a near MMSE-based performance. Our numerical results indicate that SURE-TISTA is superior to TISTA and other traditional algorithms in many aspects, which can be promising in image denoising. |
| Databáze: | OpenAIRE |
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