Image Compressed Sensing

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (23 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
  1 Dec 2022 4 repositories listed Syntology ran 12 of 23 samples · 11 unverified
  Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators.
• Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing
  11 Mar 2025 1 repository listed Syntology ran 1 of 4 samples · 3 unverified
  In this paper, we propose to use the powerful prior knowledge of pre-trained diffusion model in DUNs to achieve high-quality reconstruction with less steps for image CS.
• Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problems
  21 Nov 2024 1 repository listed
  In this paper, we propose a differentiable SVD based on the Moore-Penrose pseudoinverse to address this issue.
• Invertible Diffusion Models for Compressed Sensing
  25 Mar 2024 1 repository listed
  To enable such memory-intensive end-to-end fine-tuning, we propose a novel two-level invertible design to transform both (1) multi-step sampling process and (2) noise estimation U-Net in each step into invertible…
• Deep Physics-Guided Unrolling Generalization for Compressed Sensing
  18 Jul 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  By absorbing the merits of both the model- and data-driven methods, deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction.
• Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Network
  28 Sep 2022 1 repository listed
  During the measurement period, we directly obtain all measurements from a trained measurement network, which employs fully convolutional structures and is jointly trained with the reconstruction network from the input…
• Content-aware Scalable Deep Compressed Sensing
  19 Jul 2022 1 repository listed
  To more efficiently address image compressed sensing (CS) problems, we present a novel content-aware scalable network dubbed CASNet which collectively achieves adaptive sampling rate allocation, fine granular…
• Global Sensing and Measurements Reuse for Image Compressed Sensing
  23 Jun 2022 1 repository listed
  However, existing methods obtain measurements only from partial features in the network and use them only once for image reconstruction.
• Image Compressed Sensing Using Non-local Neural Network
  7 Dec 2021 1 repository listed
  In this paper, a novel image CS framework using non-local neural network (NL-CSNet) is proposed, which utilizes the non-local self-similarity priors with deep network to improve the reconstruction quality.
• Sequential Convolution and Runge-Kutta Residual Architecture for Image Compressed Sensing
  1 Aug 2020 1 repository listed
  In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods.
• Scalable Convolutional Neural Network for Image Compressed Sensing
  1 Jun 2019 1 repository listed
  Compared with the existing deep learning based image CS methods, SCSNet achieves scalable sampling and quality scalable reconstruction at any sampling ratio with only one model.
• Deep neural network based sparse measurement matrix for image compressed sensing
  19 Jun 2018 1 repository listed
  In this paper, a Deep neural network based Sparse Measurement Matrix (DSMM) is learned by the proposed convolutional network to reduce the sampling computational complexity and improve the CS reconstruction performance.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.