A large-scale, high-quality training dataset for single-image super-resolution (SISR), filtered from nyuuzyou/pxhere using the LUCID filtering pipeline. All source images are CC0-licensed (public domain).

Overview

Property	Value
Source	PxHere (CC0) via WebDataset tars
Filtering	LUCID pipeline (ICNet complexity + signal filter + deduplication)
Tile size	256×256 pixels
Multiscale	Yes (1.0×, 0.75×, 0.5×, 0.25× scales)
Complexity threshold	≥ 0.6 (LUCID auto-calibrated)
Deduplication	Cosine similarity < 0.96
License	CC0-1.0 (public domain)
Total tiles	1,590,938
Disk size	199 GB

Intended Use

This dataset is designed for training SISR models from scratch, particularly large transformer-based architectures that are data-hungry:

HAT (Hybrid Attention Transformer)
HAT-L (Large variant)
SwinIR
RealESRGAN / traiNNer-redux
Diffusion-based super-resolution models
Any new architecture that benefits from diverse, high-quality training data

Recommended Training Strategy

This dataset is part of a three-stage training pipeline:

Stage	Dataset	Purpose
1. Pretrain from scratch	This dataset (lucid-cc0-v2)	Learn general image representations from diverse CC0 photos
2. Finetune	lucid-cc0-v2-hc (high-complexity, 256×256)	Refine on highest-quality, most detailed tiles
3. Finetune-finetune	lucid-cc0-v2-hc-512 (high-complexity, 512×512)	Push quality with maximum patch size

Dataset Structure

lucid-cc0-v2/
├── train/
│   ├── 000/          # ≤10,000 tiles per subdirectory
│   │   ├── 00000.png
│   │   ├── 00001.png
│   │   └── ...
│   ├── 001/
│   └── ...
├── LR/
│   ├── x2/           # Bicubic downscaled ×2 (MATLAB-compatible)
│   └── x4/           # Bicubic downscaled ×4 (MATLAB-compatible)
├── batch_manifest.json
├── lineage_batch_*.csv
└── DATASET_NOTES.md

Filtering Pipeline

Images were filtered using LUCID with the following stages:

Signal filter — Removes low-information images (blurry, overexposed, underexposed, low-contrast)
ICNet complexity scoring — Neural network estimates perceptual complexity; tiles below threshold are removed
Multiscale tiling — Images tiled at multiple scales (1.0×, 0.75×, 0.5×, 0.25×) to capture both fine detail and global structure
Deduplication — Perceptual cosine similarity deduplication (threshold 0.96) removes near-duplicate tiles
Tile extraction — 256×256 PNG tiles saved with ≤10,000 files per subdirectory

Source Data

Repository: nyuuzyou/pxhere
Description: ~1.1M CC0 images from PxHere, stored as WebDataset tars
Content: Professional photography spanning landscapes, architecture, nature, objects, and more
License: CC0-1.0 (public domain)

Bicubic Downscaling

LR (low-resolution) images are provided alongside HR tiles, downscaled using MATLAB-compatible bicubic interpolation (a = -0.5 anti-aliased cubic kernel). This matches the standard used in SISR benchmarks (Urban100, Set5, Set14, etc.) and ensures comparable PSNR/SSIM values.

Scale factors: ×2 and ×4.

Lineage

Each batch produces a lineage_batch_*.csv file tracking per-image complexity scores and tile counts for reproducibility.

Citation

If you use this dataset, please cite:

bibtex

@dataset{lucid_cc0_v2,
  title={LUCID-CC0 v2: Large-Scale Curated CC0 Training Dataset for SISR},
  author={Phips},
  year={2026},
  license={CC0-1.0},
  url={https://huggingface.co/datasets/Phips/lucid-cc0-v2}
}

Acknowledgments

Source images from PxHere (CC0)
Filtering powered by LUCID
Inspired by the SISR community's need for large-scale, ethically-sourced training data