lucid-cc0-v2-hc

LUCID-CC0 v2 High Complexity (256×256): Finetuning Dataset for SISR

A high-complexity subset of lucid-cc0-v2 containing only the most detailed, information-rich tiles. Designed for finetuning pretrained SISR models to push final quality metrics.

Overview

Intended Use

This dataset is designed for finetuning SISR models that have been pretrained on the full lucid-cc0-v2 dataset. The high-complexity tiles contain the sharpest edges, finest textures, and most intricate details — exactly what a model needs to refine its super-resolution capabilities.

Recommended Training Strategy

Why High Complexity?

• Transformer models (HAT, SwinIR) are data-hungry but also benefit from quality-focused finetuning
• High-complexity tiles contain more high-frequency information per pixel
• Finetuning on these tiles specifically improves texture recovery and edge sharpness
• The 256×256 patch size is compatible with most SISR training frameworks

Dataset Structure

lucid-cc0-v2-hc/
├── train/
│   ├── 000/
│   │   ├── 00000.png
│   │   └── ...
│   ├── 001/
│   └── ...
├── LR/
│   ├── x2/           # Bicubic downscaled ×2
│   └── x4/           # Bicubic downscaled ×4
└── metrics.csv       # Per-image complexity statistics

Filtering Criteria

Tiles were selected from lucid-cc0-v2 based on ICNet complexity score:

• Threshold: ≥ 0.85 (out of 1.0)
• Distribution: Mean 0.917, all tiles above 0.85
• What this captures: Images with strong edges, fine textures, intricate patterns, high local contrast
• What this excludes: Smooth gradients, blurry regions, low-detail areas, sky/water surfaces

Bicubic Downscaling

LR images are provided using MATLAB-compatible bicubic interpolation, matching the standard used in SISR benchmarks.

Scale factors: ×2 and ×4.

Citation

@dataset{lucid_cc0_v2_hc,
  title={LUCID-CC0 v2 High Complexity: Finetuning Dataset for SISR},
  author={Phips},
  year={2026},
  license={CC0-1.0},
  url={https://huggingface.co/datasets/Phips/lucid-cc0-v2-hc}
}