Datasets:
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---
license: cc
language:
- en
tags:
- INR
- 2d
- 3d
- image
- voxel
size_categories:
- 1M<n<10M
configs:
- config_name: div2k
data_files:
- split : '0'
path: "DIV2K/0064.png"
- split : '1'
path: "DIV2K/0007.png"
- split : '2'
path: "DIV2K/0010.png"
- split : '3'
path: "DIV2K/0029.png"
- split : '4'
path: "DIV2K/0063.png"
- split : '5'
path: "DIV2K/0072.png"
- split : '6'
path: "DIV2K/0079.png"
- split : '7'
path: "DIV2K/0088.png"
- split : '8'
path: "DIV2K/0093.png"
- split : '9'
path: "DIV2K/0131.png"
- config_name: ct
data_files:
- split : '1234'
path: "chest.png"
- config_name: spheres
data_files:
- split: "1234" # seed
path: "SparseSphereSignal/1234/*.npy"
- split: "2024" # seed
path: "SparseSphereSignal/2024/*.npy"
- split: "5678" # seed
path: "SparseSphereSignal/5678/*.npy"
- split: "7618" # seed
path: "SparseSphereSignal/7618/*.npy"
- split: "7890" # seed
path: "SparseSphereSignal/7890/*.npy"
- config_name: bandlimited
data_files:
- split: "1234" # seed
path: "BandlimitedSignal/1234/*.npy"
- split: "2024" # seed
path: "BandlimitedSignal/2024/*.npy"
- split: "5678" # seed
path: "BandlimitedSignal/5678/*.npy"
- split: "7618" # seed
path: "BandlimitedSignal/7618/*.npy"
- split: "7890" # seed
path: "BandlimitedSignal/7890/*.npy"
- config_name: sierpinski
data_files:
- split: "0.1" # seed
path: "sierpinski_triangle/*0.npy"
- split: "0.2" # seed
path: "sierpinski_triangle/*1.npy"
- split: "0.3" # seed
path: "sierpinski_triangle/*2.npy"
- split: "0.4" # seed
path: "sierpinski_triangle/*3.npy"
- split: "0.5" # seed
path: "sierpinski_triangle/*4.npy"
- split: "0.6" # seed
path: "sierpinski_triangle/*5.npy"
- split: "0.7" # seed
path: "sierpinski_triangle/*6.npy"
- split: "0.8" # seed
path: "sierpinski_triangle/*7.npy"
- split: "0.9" # seed
path: "sierpinski_triangle/*8.npy"
- config_name: star_target
data_files:
- split : '1234'
path: "star_resolution_target.npy"
---
# Signal Dataset Loader
This repository provides a small collection of synthetic and real signals—both 2D and 3D—used for compression, reconstruction.
---
## Quick Start
All classes share the call signature
```
(dimension, length, bandlimit, seed, generate=True, super_resolution=False, sparse=False)
```
* **dimension** – 2 or 3 (ignored when not applicable)
* **length** – 1000
* **bandlimit** – fractional control variable (0.1 – 0.9 in 0.1 increments for `BandlimitedSignal`, `SparseSphereSignal`, and `Sierpinski`; interpretation varies per class)
* **seed** – ensures deterministic generation and consistent file paths (for `BandlimitedSignal` and `SparseSphereSignal` the repository ships five predefined seeds: 1234, 2024, 5678, 7890, 7618)
* **generate** – `True` = create new signal, `False` = load cached `.npy`
* **super\_resolution / sparse** – optional toggles (see catalog below)
---
## Signal Catalog
### Synthetic Signals (\~1 M values each)
| Class | Dim | Description |
| ---------------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------- |
| **BandlimitedSignal** | 2D / 3D | Uniform noise passed through a circular low‑pass filter; nine preset cut‑offs yield progressively higher spatial frequencies |
| **SparseSphereSignal** | 2D / 3D | Random circles/spheres occupying a fixed volume fraction; sphere radius inversely proportional to `bandlimit` |
| **Sierpinski** | 2D | Classic Sierpinski triangle rendered at depths 0 – 9, depth = `int(bandlimit*10)−1` |
| **StarTarget** | 2D | Star‑shaped resolution target with alternating wedges; default 40 solid wedges (80 spokes total) |
### Real‑World Signals
| Class | Notes |
| ------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- |
| **RealImage** | Ten DIV2K images (`DIV2K/00xx{,x4}.png`). `super_resolution=False` loads the bicubic ×4 LR image; `True` loads the HR counterpart |
| **Voxel\_Fitting** | Stanford Dragon voxel grid. `sparse=True` keeps only surface voxels; `False` loads full occupancy. `super_resolution` picks a higher‑res scan |
| **CTImage** | Single axial chest CT slice (`chest.png`), loaded as grayscale float32 |
---
## Adding Your Own Signal
1. Subclass the same pattern and expose a `self.signal` NumPy array.
2. Save deterministic outputs to `<ClassName>/<seed>/` so they can be re‑loaded with `generate=False`.
3. Keep the in‑memory footprint under \~1 M elements for apples‑to‑apples comparisons.
---
## Citation & Licensing
If you use this loader in academic or industrial work, please cite:
```bibtex
@article{kim2025grids,
title = {Grids Often Outperform Implicit Neural Representations},
author = {Kim, Namhoon and Fridovich-Keil, Sara},
journal = {arXiv preprint arXiv:2506.11139},
year = {2025}
}
```
Code and synthetic assets are released under the Creative Commons **CC‑BY‑4.0** license. Real images remain subject to the terms of their original datasets (e.g., DIV2K). |