Block Image Compressive Sensing with Local and Global Information Interaction
Authors: Xiaoyu Kong, Yongyong Chen, Feng Zheng, Zhenyu He
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show our BRBCN method outperforms existing stateof-the-art methods by a large margin. |
| Researcher Affiliation | Academia | 1 Harbin Institute of Technology (Shenzhen) 2 Southern University of Science and Technology |
| Pseudocode | No | The paper describes the proposed method in prose and provides diagrams, but it does not include a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/XYkong-CS/BRBCN |
| Open Datasets | Yes | We use Image Net to train BRBCN and all images are converted to gray-scale and resized into 256 256. (...) Two gray-scale datasets including Set14 (14 images) (Zeyde, Elad, and Protter 2010) and BSD68 (68 images) (Sapiro 2008) and one color dataset Waterloo (4744 images) (Ma et al. 2016) are used. |
| Dataset Splits | No | The paper mentions using specific datasets for training and testing but does not explicitly provide information about training/validation/test dataset splits (e.g., percentages or counts for each split). |
| Hardware Specification | Yes | We implement the model using Py Torch, and train and test it on Nvidia RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the software used for implementation but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | Yes | The training epochs and batch size are three and eight, respectively. The Adam optimization strategy is applied, with a learning rate of 10 4 for the early two epochs and then reduced to 10 5 for the last epoch. Five sampling ratios are investigated, including low ratios 0.01 and 0.04, middle ratios 0.1 and 0.25, and a higher ratio 0.5. The default iteration time K and block size B are set to be 8 and 32. The tokens dimension CT is set as 128. |