This reposiotry is for AutoBCS framwork introduced in the following paper: https://arxiv.org/abs/2009.14706, which has been accepted by IEEE transactions on cybernetics (https://doi.org/10.1109/TCYB.2021.3127657).

This code was built and tested on Centos 7.8 with Nvdia Tesla V100 and Windows 10 environment (python 3.7, pytorch > 1.1) with GTX 1060.

Abstract—Block compressive sensing is a well-known signal acquisition and reconstruction paradigm with widespread application prospects in science, engineering and cybernetic systems. However, state-of-the-art block-based image compressive sensing (BCS) methods generally suffer from two issues. The sparsifying domain and the sensing matrices widely used for image acquisition are not data-driven, and thus both the features of the image and the relationships among subblock images are ignored. Moreover, doing so requires addressing high-dimensional optimization problems with extensive computational complexity for image reconstruction. In this paper, we provide a deep learning strategy for BCS, called AutoBCS, which takes the prior knowledge of images into account in the acquisition step and establishes a subsequent reconstruction model for performing fast image reconstruction with a low computational cost. More precisely, we present a learning-based sensing matrix (LSM) derived from training data to accomplish image acquisition, thereby capturing and preserving more image characteristics than those captured by existing methods. In particular, the generated LSM is proven to satisfy the theoretical requirements of compressive sensing, such as the so-called restricted isometry property. Additionally, we build a noniterative reconstruction network, which provides an end-to-end BCS reconstruction framework to eliminate blocking artifacts and maximize image reconstruction accuracy, in our AutoBCS architecture. Furthermore, we investigate comprehensive comparison studies with both traditional BCS approaches and newly developed deep learning methods. Compared with these approaches, our AutoBCS framework can not only provide superior performance in terms of image quality metrics (SSIM and PSNR) and visual perception, but also automatically benefit reconstruction speed.

Fig. 1: Schematic representation of our proposed AutoBCS architecture. AutoBCS replaces the traditional BCS approach with a unified image acquisition and reconstruction framework.

Fig. 2: The deep neural network architecture of AutoBCS contains two components: a data-driven image acquisition module and a noniterative data reconstruction module (composed of an initial reconstruction subnetwork and an octave reconstruction subnetwork).

Python 3.7 or later
NVDIA GPU (CUDA 10.0)
Pytorch 1.10 or later
MATLAB 2017b or later

1. Clone this repository

    git clone https://github.com/YangGaoUQ/AutoBCS.git

2. Run the following scripts (in Folder './Inference/') to test the pre-trained models.

    python Evaluate_set5.py

1. Prepare your test data, and make your own directory for it, and rename them in a numerical order. (You can use Prepare_TestData.m provided in the folder './set5/' to process your data.)

    matlab -r "Prepare_TestData.m"

2. Modify the test code.

   1. Open ./Inference/Evaluate_set5.py using your own IDE
   2. go to line 37, set File_No = numer_of_your_own_images
   3. go to line 38, change 'set5' to your own directory
   4. save it as your own test script file.

3. Run your own code

    python your_own_test_script.py  

1. prepare your own trianing datasets (We used BSD500 database https://github.com/BIDS/BSDS500 )

2. Preprocessing data sets using the codes in the directory './Preprocessing_for_training' with Matlab

    matlab -r "GenerateData_model_64_96_Adam.m"

3. Enter the tranining folder ('./Training/'), and run the code:

    python TrainAutoBCS.py