AMPA-Net: Optimization-Inspired Attention Neural Network for Deep Compressed Sensing

14 Oct 2020  ·  Nanyu Li, Charles C. Zhou ·

Compressed sensing (CS) is a challenging problem in image processing due to reconstructing an almost complete image from a limited measurement. To achieve fast and accurate CS reconstruction, we synthesize the advantages of two well-known methods (neural network and optimization algorithm) to propose a novel optimization inspired neural network which dubbed AMP-Net... AMP-Net realizes the fusion of the Approximate Message Passing (AMP) algorithm and neural network. All of its parameters are learned automatically. Furthermore, we propose an AMPA-Net which uses three attention networks to improve the representation ability of AMP-Net. Finally, We demonstrate the effectiveness of AMP-Net and AMPA-Net on four standard CS reconstruction benchmark data sets. Our code is available on read more

PDF Abstract
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Compressive Sensing BSD68 CS=50% AMPA-Net Average PSNR 36.33 # 1
Compressive Sensing BSDS100 - 2x upscaling AMPA-Net Average PSNR 35.95 # 1
Compressive Sensing Set11 cs=50% AMPA-Net Average PSNR 40.32 # 1
Compressive Sensing Urban100 - 2x upscaling AMPA-Net Average PSNR 35.86 # 1


No methods listed for this paper. Add relevant methods here