Image Compressed Sensing
9 papers with code • 4 benchmarks • 4 datasets
ImageNet
CelebA
CBSD68
Set11
Most implemented papers
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
wyhuai/ddnm
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Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators.
Deep neural network based sparse measurement matrix for image compressed sensing
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.
Scalable Convolutional Neural Network for Image Compressed Sensing
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.
Image Compressed Sensing Using Non-local Neural Network
wenxuecui/nl-csnet-pytorch
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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.
Global Sensing and Measurements Reuse for Image Compressed Sensing
fze0012/mr-ccsnet
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CVPR 2022
However, existing methods obtain measurements only from partial features in the network and use them only once for image reconstruction.
Content-aware Scalable Deep Compressed Sensing
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 scalability and high-quality reconstruction.
Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Network
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 image.
Deep Physics-Guided Unrolling Generalization for Compressed Sensing
By absorbing the merits of both the model- and data-driven methods, deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction.
