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Physics-Inspired Compressive Sensing: Beyond deep unrolling

https://doi.org/10.1109/msp.2022.3208394

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing

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Invertible Image Rescaling

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Deep Decomposition Learning for Inverse Imaging Problems

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An efficient augmented Lagrangian method with applications to total variation minimization

inferredcites →https://doi.org/10.1016/j.acha.2009.04.002openalex-snapshot

Iterative hard thresholding for compressed sensing

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iPiano-Net: Nonconvex optimization inspired multi-scale reconstruction network for compressed sensing

inferredcites →https://doi.org/10.1017/9781009042529.005openalex-snapshot

Model-Based Reconstruction with Learning: From Unsupervised to Supervised and Beyond

inferredcites →https://doi.org/10.1017/9781009042529.006openalex-snapshot

Deep Algorithm Unrolling for Biomedical Imaging

inferredcites →https://doi.org/10.1073/pnas.0909892106openalex-snapshot

Message-passing algorithms for compressed sensing

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Nonlinear image recovery with half-quadratic regularization

inferredcites →https://doi.org/10.1109/cvpr.2016.55openalex-snapshot

ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements

inferredcites →https://doi.org/10.1109/cvpr.2018.00196openalex-snapshot

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

inferredcites →https://doi.org/10.1109/cvpr.2019.01257openalex-snapshot

Scalable Convolutional Neural Network for Image Compressed Sensing

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00069openalex-snapshot

Deep Convolutional Dictionary Learning for Image Denoising

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00849openalex-snapshot

Adaptive Consistency Prior based Deep Network for Image Denoising

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Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

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Deep Tensor ADMM-Net for Snapshot Compressive Imaging

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SwinIR: Image Restoration Using Swin Transformer

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ISTA-NET ++ : Flexible Deep Unfolding Network for Compressive Sensing

inferredcites →https://doi.org/10.1109/icme51207.2021.9428320openalex-snapshot

Spatial-Temporal Synergic Prior Driven Unfolding Network for Snapshot Compressive Imaging

inferredcites →https://doi.org/10.1109/jstsp.2020.2977507openalex-snapshot

Optimization-Inspired Compact Deep Compressive Sensing

inferredcites →https://doi.org/10.1109/msp.2007.914730openalex-snapshot

Single-pixel imaging via compressive sampling

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An Introduction To Compressive Sampling [A sensing/sampling paradigm that goes against the common knowledge in data acquisition]

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Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks

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Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues

inferredcites →https://doi.org/10.1109/msp.2019.2951469openalex-snapshot

Transform Learning for Magnetic Resonance Image Reconstruction: From Model-Based Learning to Building Neural Networks

inferredcites →https://doi.org/10.1109/msp.2020.3016905openalex-snapshot

Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

inferredcites →https://doi.org/10.1109/tci.2021.3118944openalex-snapshot

Deep Equilibrium Architectures for Inverse Problems in Imaging

inferredcites →https://doi.org/10.1109/tip.2014.2329449openalex-snapshot

Compressive Sensing via Nonlocal Low-Rank Regularization

inferredcites →https://doi.org/10.1109/tip.2019.2928136openalex-snapshot

Image Compressed Sensing Using Convolutional Neural Network

inferredcites →https://doi.org/10.1109/tip.2020.3044472openalex-snapshot

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

inferredcites →https://doi.org/10.1109/tip.2021.3088611openalex-snapshot

Deep-Learned Regularization and Proximal Operator for Image Compressive Sensing

inferredcites →https://doi.org/10.1109/tip.2021.3091834openalex-snapshot

COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing

inferredcites →https://doi.org/10.1109/tip.2022.3195319openalex-snapshot

Content-Aware Scalable Deep Compressed Sensing

inferredcites →https://doi.org/10.1109/tmi.2018.2823768openalex-snapshot

Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT

inferredcites →https://doi.org/10.1109/tmi.2018.2865356openalex-snapshot

MoDL: Model-Based Deep Learning Architecture for Inverse Problems

inferredcites →https://doi.org/10.1109/tmm.2020.3014561openalex-snapshot

Multi-Channel Deep Networks for Block-Based Image Compressive Sensing

inferredcites →https://doi.org/10.1109/tpami.2015.2439281openalex-snapshot

Image Super-Resolution Using Deep Convolutional Networks

inferredcites →https://doi.org/10.1145/3474085.3475562openalex-snapshot

Memory-Augmented Deep Unfolding Network for Compressive Sensing

inferredcites →https://doi.org/10.1561/2400000003openalex-snapshot

Proximal Algorithms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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DDOAS-Net: Lightweight and interpretable compressed sensing by fusing a dual-domain physical model and a sparse prior

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GDU-net: A cross-gated dual-domain optimization unfolding network for image compressive sensing

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MMC-CS: Multi-branch multi-stage contrastive learning for self-supervised compressed sensing

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PIPO-Net: A Penalty-based Independent Parameters Optimization deep unfolding Network

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D&D-Net: A diffusion and deep priors regularized network for hyperspectral reconstruction

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