Algorithm

Materials science

Regularization (linguistics)

Image processing

Iterative reconstruction

Parallel computing

Computer science

Matrix (chemical analysis)

Speedup

Pattern recognition (psychology)

Image (mathematics)

Image denoising

Artificial intelligence

Composite material

Deep learning

Compressed sensing

Noise reduction

Noise (video)

Iterative method

Computer vision

IEEE Transactions on Image Processing

Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding methods have the good interpretation of model-based methods and the high speed of classical deep network methods. In this article, to solve the visual image CS problem, we propose a deep unfolding model dubbed AMP-Net. Rather than learning regularization terms, it is established by unfolding the iterative denoising process of the well-known approximate message passing algorithm. Furthermore, AMP-Net integrates deblocking modules in order to eliminate the blocking artifacts that usually appear in CS of visual images. In addition, the sampling matrix is jointly trained with other network parameters to enhance the reconstruction performance. Experimental results show that the proposed AMP-Net has better reconstruction accuracy than other state-of-the-art methods with high reconstruction speed and a small number of network parameters.

AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing