| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-image |
| tags: |
| - image-restoration |
| - defocus-deblurring |
| - microscopy |
| - pathology |
| - neural-operator |
| - pytorch |
| --- |
| |
| # DGNO — Discontinuous Galerkin Neural Operator |
|
|
| Pretrained checkpoints **and** the preprocessed BBBC006 dataset for |
| **"Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurring"** (ICML 2026). |
|
|
| - **Code & full usage:** https://github.com/Duane245/DGNO |
| - **Task:** microscopy / pathology defocus deblurring |
|
|
| DGNO comes in two operator variants — **DGNO-Face** (face-wise discontinuous Galerkin |
| operator) and **DGNO-Cell** (cell-wise) — both produced by a single training architecture |
| and loaded by a unified evaluation architecture for inference. |
|
|
| ## Checkpoints |
|
|
| Six checkpoints — three datasets × two variants. Each `.pth` stores both the raw weights |
| (`params`) and the EMA weights (`params_ema`). |
|
|
| | Dataset | DGNO-Face | DGNO-Cell | |
| |---|---|---| |
| | BBBC006 w1 | `pretrained/DGNO_BBBC006_w1_Face.pth` | `pretrained/DGNO_BBBC006_w1_Cell.pth` | |
| | BBBC006 w2 | `pretrained/DGNO_BBBC006_w2_Face.pth` | `pretrained/DGNO_BBBC006_w2_Cell.pth` | |
| | 3DHistech | `pretrained/DGNO_3DHistech_Face.pth` | `pretrained/DGNO_3DHistech_Cell.pth` | |
|
|
| Download all six into `pretrained/`: |
|
|
| ```bash |
| pip install -U huggingface_hub |
| huggingface-cli download Duane245/DGNO --include "pretrained/*" --local-dir . |
| ``` |
|
|
| Or a single checkpoint: |
|
|
| ```bash |
| huggingface-cli download Duane245/DGNO pretrained/DGNO_3DHistech_Face.pth --local-dir . |
| ``` |
|
|
| ## BBBC006 dataset |
|
|
| `BBBC006.tar.gz` (~3.4 GB) is the preprocessed BBBC006 set used in the paper — fluorescence |
| microscopy, single-channel grayscale, with both the `w1` (Hoechst-stained nuclei) and `w2` |
| (Phalloidin-stained actin) channels in one archive. |
|
|
| ```bash |
| huggingface-cli download Duane245/DGNO BBBC006.tar.gz --local-dir datasets |
| tar -xzf datasets/BBBC006.tar.gz -C datasets && rm datasets/BBBC006.tar.gz |
| ``` |
|
|
| Extraction yields `datasets/BBBC006/{train,test}/{GT,blur}/*.tif`, ready for training and |
| testing with no further preprocessing; the `w1` / `w2` split is selected at run time. |
|
|
| ## Results |
|
|
| | Dataset | Variant | PSNR ↑ | SSIM ↑ | LPIPS ↓ | |
| |---|---|---|---|---| |
| | BBBC006 w1 | DGNO-Cell | 37.22 | 0.959 | 0.103 | |
| | BBBC006 w1 | DGNO-Face | 37.09 | 0.958 | 0.104 | |
| | BBBC006 w2 | DGNO-Face | 32.66 | 0.847 | 0.323 | |
| | BBBC006 w2 | DGNO-Cell | 32.54 | 0.848 | 0.322 | |
| | 3DHistech | DGNO-Face | 34.02 | 0.890 | 0.095 | |
| | 3DHistech | DGNO-Cell | 34.00 | 0.890 | 0.093 | |
|
|
| ## License |
|
|
| Apache-2.0. BBBC006 imagery originates from the |
| [Broad Bioimage Benchmark Collection](https://bbbc.broadinstitute.org/BBBC006). |
| See the [GitHub repository](https://github.com/Duane245/DGNO) for the citation and details. |
|
|
