Sample Dataset and Trained Model Parameters for Back-Projection Diffusion

We have uploaded a sample dataset for training and testing Back-Projection Diffusion. Trained model parameters for the dataset are also provided in tmp.zip. For a formal description of the dataset, please refer to our preprint: Borong Zhang, Martín Guerra, Qin Li, and Leonardo Zepeda-Núñez. "Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models." arXiv preprint arXiv:2408.02866 (2024). In 10hsquares_trainingdata and 10hsquares_testdata, perturbations are stored as eta.h5 with the following structure: eta.h5/ ├── /eta The scattering data are stored as scatter.h5, or as scatter_order_n.h5 (n indicates the order of the stencil used for data generation) with the following structure: scatter.h5/ ├── /scatter_imag_freq_1 ├── /scatter_real_freq_1 ├── /scatter_imag_freq_2 ├── /scatter_real_freq_2 ├── /scatter_imag_freq_3 ├── /scatter_real_freq_3 The tmp folder contains the trained model parameters. For usage instructions, please refer to our GitHub repository: https://github.com/borongzhang/back_projection_diffusion If this dataset is useful to your research, please cite our preprint:@misc{zhang2024backprojectiondiffusionsolvingwideband, title={Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models}, author={Borong Zhang and Martín Guerra and Qin Li and Leonardo Zepeda-Núñez}, year={2024}, eprint={2408.02866}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2408.02866}, }