---

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 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 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 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 (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 (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 (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

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)

# 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.