README.md

---
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).