MB-DAMPNet: a novel multi-branch denoising-based approximate message passing algorithm via deep neural network for image reconstruction

DOI10.1088/1361-6420/ac1bffzbMath1475.94027OpenAlexW3191431068MaRDI QIDQ5157861

Weiguang Jia, Chunle Guo, Xiangjun Yin, Jichang Guo, Huihui Yue

Publication date: 20 October 2021

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1361-6420/ac1bff

zbMATH Keywords

image reconstructioncompressive sensingdeep neural networkapproximate message passing algorithm

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• Sparsity-enforcing regularisation and ISTA revisited
• From Denoising to Compressed Sensing
• Recovery of sparse signals using OMP and its variants: convergence analysis based on RIP
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
• Total variation superiorized conjugate gradient method for image reconstruction
• Subspace Pursuit for Compressive Sensing Signal Reconstruction
• Image Compressed Sensing Using Convolutional Neural Network
• Denoising AMP for MRI Reconstruction: BM3D-AMP-MRI

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