PIPO-Net: A Penalty-based Independent Parameters Optimization Deep Unfolding Network

TL;DR

PIPO-Net is a deep unfolding network that optimizes independent parameters in each module to improve compressive sensing image reconstruction, combining interpretability with high accuracy.

Contribution

It introduces a novel penalty-based deep unfolding network with independent parameter optimization for each module, enhancing flexibility and performance.

Findings

Effective in reconstructing CS images

Outperforms existing methods in accuracy

Increases interpretability of deep CS models

Abstract

Compressive sensing (CS) has been widely applied in signal and image processing fields. Traditional CS reconstruction algorithms have a complete theoretical foundation but suffer from the high computational complexity, while fashionable deep network-based methods can achieve high-accuracy reconstruction of CS but are short of interpretability. These facts motivate us to develop a deep unfolding network named the penalty-based independent parameters optimization network (PIPO-Net) to combine the merits of the above mentioned two kinds of CS methods. Each module of PIPO-Net can be viewed separately as an optimization problem with respective penalty function. The main characteristic of PIPO-Net is that, in each round of training, the learnable parameters in one module are updated independently from those of other modules. This makes the network more flexible to find the optimal solutions of the corresponding problems. Moreover, the mean-subtraction sampling and the high-frequency complementary blocks are developed to improve the performance of PIPO-Net. Experiments on reconstructing CS images demonstrate the effectiveness of the proposed PIPO-Net.