# DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing

arXiv: https://arxiv.org/abs/1702.05743 · HTML: https://mlsanity.com/paper/1702.05743/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2017.

## Built on by (most cited first, 30 of 30)

- [Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems](https://mlsanity.com/paper/2211.12343/) (2022)
- [Quantized Compressed Sensing with Score-based Generative Models](https://mlsanity.com/paper/2211.13006/) (2022)
- [CSformer: Bridging Convolution and Transformer for Compressive Sensing](https://mlsanity.com/paper/2112.15299/) (2021)
- [Image Compressed Sensing Using Non-local Neural Network](https://mlsanity.com/paper/2112.03712/) (2021)
- [Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging](https://mlsanity.com/paper/2505.23180/) (2025)
- [Dual-Scale Transformer for Large-Scale Single-Pixel Imaging](https://mlsanity.com/paper/2404.05001/) (2024)
- [JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging](https://mlsanity.com/paper/2205.07770/) (2022)
- [Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing](https://mlsanity.com/paper/2208.01827/) (2022)
- [Deep Unfolding Network for Image Compressed Sensing by Content-adaptive Gradient Updating and Deformation-invariant Non-local Modeling](https://mlsanity.com/paper/2310.10033/) (2023)
- [ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data](https://mlsanity.com/paper/2103.09203/) (2021)
- [Dynamic Proximal Unrolling Network for Compressive Imaging](https://mlsanity.com/paper/2107.11007/) (2021)
- [Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks](https://mlsanity.com/paper/2204.11640/) (2022)
- [DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds](https://mlsanity.com/paper/2205.07050/) (2022)
- [Adaptive and Cascaded Compressive Sensing](https://mlsanity.com/paper/2203.10779/) (2022)
- [Robust Deep Compressive Sensing with Recurrent-Residual Structural Constraints](https://mlsanity.com/paper/2207.07301/) (2022)
- [Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction](https://mlsanity.com/paper/2304.06955/) (2023)
- [Efficient Physics-Based Learned Reconstruction Methods for Real-Time 3D Near-Field MIMO Radar Imaging](https://mlsanity.com/paper/2312.16959/) (2023)
- [MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS)](https://mlsanity.com/paper/2401.02884/) (2024)
- [Deep Network for Image Compressed Sensing Coding Using Local Structural Sampling](https://mlsanity.com/paper/2402.19111/) (2024)
- [Sampling Innovation-Based Adaptive Compressive Sensing](https://mlsanity.com/paper/2503.13241/) (2025)
- [Scalable Deep Compressive Sensing](https://mlsanity.com/paper/2101.08024/) (2021)
- [Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks](https://mlsanity.com/paper/2103.08482/) (2021)
- [Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective](https://mlsanity.com/paper/2105.13191/) (2021)
- [Image Compressed Sensing with Multi-scale Dilated Convolutional Neural Network](https://mlsanity.com/paper/2209.13761/) (2022)
- [JSRNN: Joint Sampling and Reconstruction Neural Networks for High Quality Image Compressed Sensing](https://mlsanity.com/paper/2211.05963/) (2022)
- [A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing](https://mlsanity.com/paper/2304.11674/) (2023)
- [NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing](https://mlsanity.com/paper/2305.03899/) (2023)
- [How to warm-start your unfolding network](https://mlsanity.com/paper/2502.01854/) (2025)
- [Physics-guided Deep Unfolding Network for Enhanced Kronecker Compressive sensing](https://mlsanity.com/paper/2508.09528/) (2025)
- [Beyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing](https://mlsanity.com/paper/2606.03666/) (2026)

## Abstract (arXiv)

Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been reported, which dramatically reduce the time complexity than iterative reconstruction algorithms. In this paper, we propose a novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed (CS) measurement. The DR2-Net is proposed based on two observations: 1) linear mapping could reconstruct a high-quality preliminary image, and 2) residual learning could further improve the reconstruction quality. Accordingly, DR2-Net consists of two components, i.e., linear mapping network and residual network, respectively. Specifically, the fully-connected layer in neural network implements the linear mapping network. We then expand the linear mapping network to DR2-Net by adding several residual learning blocks to enhance the preliminary image. Extensive experiments demonstrate that the DR2-Net outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.04, 0.1, and 0.25, respectively. The code of DR2-Net has been released on: https://github.com/coldrainyht/caffedr2