Image Compressed Sensing

8 papers with code • 4 benchmarks • 4 datasets

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Most implemented papers

Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

wyhuai/ddnm 1 Dec 2022

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

WenxueCui/DSMM 19 Jun 2018

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

wzhshi/SCSNet CVPR 2019

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 7 Dec 2021

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 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

guaishou74851/casnet 19 Jul 2022

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

ccnuzfw/msdcnn 28 Sep 2022

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.