https://github.com/guaishou74851/pcnet
(TPAMI 2024) Practical Compact Deep Compressed Sensing [PyTorch]
https://github.com/guaishou74851/pcnet
algorithm-unrolling compressed-sensing compressive-sampling compressive-sensing computational-imaging computer-vision deep-learning deep-neural-networks deep-unfolding image-reconstruction image-restoration python pytorch sampling-matrix single-pixel-imaging structural-reparameterization
Last synced: 19 days ago
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(TPAMI 2024) Practical Compact Deep Compressed Sensing [PyTorch]
- Host: GitHub
- URL: https://github.com/guaishou74851/pcnet
- Owner: Guaishou74851
- Created: 2023-01-15T12:22:38.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-09T06:51:52.000Z (about 2 months ago)
- Last Synced: 2025-04-10T01:07:46.528Z (19 days ago)
- Topics: algorithm-unrolling, compressed-sensing, compressive-sampling, compressive-sensing, computational-imaging, computer-vision, deep-learning, deep-neural-networks, deep-unfolding, image-reconstruction, image-restoration, python, pytorch, sampling-matrix, single-pixel-imaging, structural-reparameterization
- Language: Python
- Homepage:
- Size: 9.12 MB
- Stars: 86
- Watchers: 1
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# (TPAMI 2024) Practical Compact Deep Compressed Sensing [PyTorch]
[](https://ieeexplore.ieee.org/document/10763443) [](https://arxiv.org/abs/2411.13081) 
[Bin Chen](https://scholar.google.com/citations?hl=en&user=aZDNm98AAAAJ) and [Jian Zhang](https://jianzhang.tech/)†
*School of Electronic and Computer Engineering, Peking University, Shenzhen, China.*
† Corresponding author
Accepted for publication in [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34) (TPAMI) 2024.
⭐ If PCNet is helpful to you, please star this repo. Thanks! 🤗
## 📝 Abstract
Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception. In this paper, we propose a new practical and compact network dubbed PCNet for general image CS. Specifically, in PCNet, a novel collaborative sampling operator is designed, which consists of a deep conditional filtering step and a dual-branch fast sampling step. The former learns an implicit representation of a linear transformation matrix into a few convolutions and first performs adaptive local filtering on the input image, while the latter then uses a discrete cosine transform and a scrambled block-diagonal Gaussian matrix to generate under-sampled measurements. Our PCNet is equipped with an enhanced proximal gradient descent algorithm-unrolled network for reconstruction. It offers flexibility, interpretability, and strong recovery performance for arbitrary sampling rates once trained. Additionally, we provide a deployment-oriented extraction scheme for single-pixel CS imaging systems, which allows for the convenient conversion of any linear sampling operator to its matrix form to be loaded onto hardware like digital micro-mirror devices. Extensive experiments on natural image CS, quantized CS, and self-supervised CS demonstrate the superior reconstruction accuracy and generalization ability of PCNet compared to existing state-of-the-art methods, particularly for high-resolution images. Code is available at https://github.com/Guaishou74851/PCNet.
## 🍭 Overview
## ⚙ Environment
```shell
torch==2.2.1
numpy==1.24.4
opencv-python==4.2.0
scikit-image==0.21.0
```
## ⚡ Test
Run the following command:
```shell
python test.py --testset_name=Set11
```
The test sets are in `./data`.
The recovered results will be in `./test_out`.
The test sets CBSD68, Urban100, and DIV2K are available at https://github.com/Guaishou74851/SCNet/tree/main/data.
For easy comparison, test results of various existing image CS methods are available on [Google Drive](https://drive.google.com/drive/folders/1Lif_7N_bCyILFLac5JcOtJ9cWpGBNVCd) and [PKU Disk](https://disk.pku.edu.cn/link/AA1C2D8A08050744449CBFCAB51A846B2D).
## 🔥 Train
Download the dataset of [Waterloo Exploration Database](https://kedema.org/project/exploration/index.html) and put the `pristine_images` directory (containing 4744 `.bmp` image files) into `./data`, then run the following command:
```
python train.py
```
The log and model files will be in `./log` and `./model`, respectively.
## 😍 Results
## 🎓 Citation
If you find the code helpful in your research or work, please cite the following paper:
```
@article{chen2024practical,
title={Practical Compact Deep Compressed Sensing},
author={Chen, Bin and Zhang, Jian},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024},
}
```