| --- |
| license: apache-2.0 |
| tags: |
| - super-resolution |
| - image-restoration |
| - ocr |
| - text-enhancement |
| - gguf |
| - crispembed |
| library_name: crispembed |
| --- |
| |
| # Text Super-Resolution & Restoration GGUF Models |
|
|
| Lightweight super-resolution and image restoration models converted to GGUF for [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) OCR preprocessing. |
|
|
| ## Models |
|
|
| | File | Architecture | Params | Scale | Size | License | Paper | |
| |------|-------------|--------|-------|------|---------|-------| |
| | `tbsrn-telescope-f16.gguf` | TBSRN (text-line SR) | 1.13M | 2x | 2.2 MB | Apache-2.0 | CVPR 2021 | |
| | `pan-x4-f16.gguf` | PAN (pixel attention) | 272K | 4x | 0.5 MB | Apache-2.0 | ECCV 2020W | |
| | `hat-sr-x4-f16.gguf` | HAT (hybrid attention transformer) | 21M | 4x | 40 MB | MIT | CVPR 2023 | |
| | `dat-light-x2-f16.gguf` | DAT-light (dual aggregation transformer) | 830K | 2x | 38 MB | Apache-2.0 | ICCV 2023 | |
| | `restormer-denoise-f16.gguf` | Restormer (denoising) | 26M | 1x | 50 MB | Apache-2.0 | CVPR 2022 | |
|
|
| ### TBSRN Telescope (text-line SR) |
| - **Task**: Enhance individual detected text lines before recognition |
| - **Input**: Text-line crop resized to 16x64 -> **Output**: 32x128 (2x) |
| - **Source**: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) `sr_telescope` (Apache-2.0) |
|
|
| ### PAN (whole-image 4x SR) |
| - **Task**: Upscale full document pages (rescues 75dpi text) |
| - **Input**: Any RGB image (tiled) -> **Output**: 4x upscale |
| - **Source**: [PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN) `pan_x4` (Apache-2.0) |
|
|
| ### HAT (hybrid attention transformer, 4x SR) |
| - **Task**: High-quality 4x upscaling (CVPR 2023 SOTA on multiple SR benchmarks) |
| - **Input**: Any RGB image (tiled) -> **Output**: 4x upscale |
| - **Architecture**: Swin Transformer + overlapping cross-attention + channel attention |
| - **Source**: [XPixelGroup/HAT](https://github.com/XPixelGroup/HAT) (MIT) |
|
|
| ### DAT-light (dual aggregation transformer, 2x SR) |
| - **Task**: High-quality 2x upscaling with dual spatial+channel attention |
| - **Input**: Any RGB image (tiled) -> **Output**: 2x upscale |
| - **Architecture**: Split-channel windowed spatial attention + L2-normalized transposed channel attention + AIM + SGFN |
| - **Source**: [zhengchen1999/DAT](https://github.com/zhengchen1999/DAT) (Apache-2.0) |
|
|
| ### Restormer (image denoising/restoration) |
| - **Task**: Remove noise from document scans |
| - **Input**: Any RGB image -> **Output**: Denoised (same size) |
| - **Architecture**: Multi-Dconv head transposed attention, U-Net encoder-decoder |
| - **Source**: [swz30/Restormer](https://github.com/swz30/Restormer) (Apache-2.0) |
|
|
| ## Parity Verification |
|
|
| All models pass the CrispEmbed diff harness (Python reference vs C++ engine): |
|
|
| | Model | cos_sim | Status | |
| |-------|---------|--------| |
| | TBSRN | 0.999985 | PASS | |
| | PAN | 0.999654 | PASS | |
| | HAT | 0.999990 | PASS | |
| | DAT-light | 0.999956 | PASS | |
| | Restormer | 1.000000 | PASS | |
| |
| ## Usage with CrispEmbed |
| |
| ```python |
| from crispembed import CrispPanSr, CrispDatSr |
| |
| # PAN: 4x upscale |
| sr = CrispPanSr("pan-x4-f16.gguf") |
| out, ow, oh = sr.process(pixels, width, height) |
| |
| # DAT: 2x upscale (higher quality) |
| sr = CrispDatSr("dat-light-x2-f16.gguf") |
| out, ow, oh = sr.process(pixels, width, height) |
| ``` |
| |
| ```bash |
| # CLI |
| crispembed --pan-model pan-x4-f16.gguf --pan-sr input.png > output.ppm |
| crispembed --dat-model dat-light-x2-f16.gguf --dat-sr input.png > output.ppm |
| ``` |
| |
| ## License |
| |
| Apache-2.0 for all models except HAT (MIT). Both licenses are permissive. |
| |
