# ⚛  L1 Principle — Event Camera (DVS) — intensity reconstruction from asynchronous events

**ID:** `L1-078` · **Status:** ⊙ Testnet (genesis catalog)

> **🌐 Domain:** Computational Photography — *Asynchronous neuromorphic imaging*
> **🎯 Problem class:** nonlinear inverse · **🧮 Solution space:** 2D plus time video
> **📡 Carrier:** photon · **🌫 Noise:** asynchronous event
> **⚖ Difficulty (δ):** 5 · **⛓ Block:** 41554171

---

## 🧠 1. Introduction

**Event Camera (DVS) — intensity reconstruction from asynchronous events** is a **nonlinear inverse problem** whose unknown lives in **2D plus time video** space, within the **Asynchronous neuromorphic imaging** sub-domain of **Computational Photography**.

Measurements consist of photons collected by an optical detector via a **asynchronous log intensity change** sensing mechanism.

The forward operator applies, in order: L · log intensity operator; D · threshold · delta operator; S · stream · asynchronous operator.

Observations are corrupted by asynchronous event. Intensity reconstruction is ill-posed (DC level is unobservable; only log-differentials are sampled). Unique reconstruction requires an anchor frame (DAVIS hybrid output) or a strong prior (video smoothness, learned models). Contrast-threshold variance sigma_C and noise events add strong instability at low event rates.

## ⚙ 2. Forward Model

Physical chain: **x** → L · log intensity → D · threshold · delta → S · stream · asynchronous → **y** (detector).

```
y = `S.stream.asynchronous` `D.threshold.delta` `L.log_intensity` x
```

**Measurement DAG:**

| Primitive | What it does |
|---|---|
| `L.log_intensity` | L · log intensity operator |
| `D.threshold.delta` | D · threshold · delta operator |
| `S.stream.asynchronous` | S · stream · asynchronous operator |

## 🔬 3. Physics Fingerprint

| Property | Value |
|---|---|
| Domain | Computational Photography |
| Sub domain | Asynchronous neuromorphic imaging |
| Carrier | photon |
| Problem class | nonlinear_inverse |
| Solution space | 2D_plus_time_video |
| Noise model | asynchronous_event |
| Integration axis | temporal |
| Difficulty delta | 5 |
| L dag | 2.8 |

## 📡 4. Measurement Model

Intensity reconstruction is ill-posed (DC level is unobservable; only log-differentials are sampled). Unique reconstruction requires an anchor frame (DAVIS hybrid output) or a strong prior (video smoothness, learned models). Contrast-threshold variance sigma_C and noise events add strong instability at low event rates.

| Metric | Value |
|---|---|
| Metric | PSNR_dB |
| Secondary | SSIM |

## 📏 5. Operating Range (Ω)

**Center problem class:** `event_to_intensity_reconstruction` · **Forward operator:** `dvs_log_intensity_threshold`

**Center point:**

| Parameter | Unit | Value |
|---|---|---|
| H | px | 640 |
| W | px | 480 |
| N events per sec | — | 1000000 |
| Noise event rate | — | 0 |
| Contrast threshold c | — | 0.2 |
| Reconstruction rate hz | Hz | 60 |
| Contrast threshold variance | — | 0 |

**Allowed bounds:**

| Parameter | Unit | Range |
|---|---|---|
| H | px | 240 – 1280 |
| W | px | 180 – 960 |
| Hot pixels | — | 0 – 100 |
| N events per sec | — | 10000 – 100000000 |
| Noise event rate | — | 0.0 – 100000 |
| Contrast threshold c | — | 0.05 – 0.5 |
| Reconstruction rate hz | Hz | 15 – 240 |
| Refractory period drift | — | 0.0 – 1000 |
| Contrast threshold variance | — | 0.0 – 0.1 |

## 🎯 6. Tolerance (ε)

**Center tolerance:** 22.0 dB PSNR

| Metric | Range |
|---|---|
| Psnr db | 12.0 – 32.0 |

## ⚖ 7. Hardness Function

Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `4000` and δ = `5`.

## 💾 8. Reference Dataset

- **primary** · weight 1.0 · IPFS _(not pinned yet)_

## 9. On-chain Registration

- **Chain hash:** `0xa72e0d5ef4f3d0bd8dc596c336dff5a8be897f9205125c6c047f183679216e39`
- **Chain tx hash:** `0xbbdefcb2d434660bc269d96a614c31e6c00cb029ca09aaee6f9722bcd4019d72`
- **Chain block:** `41554171`

---

## File Mapping

This bundle consists of: `L1-078.md`, `L1-078.json`.

| File | Role | How to regenerate |
|------|------|-------------------|
| `L1-078.md` | Source of truth — edit this | Human or LLM |
| `L1-078.json` | Structured metadata for the registry | LLM regenerates from the sections above |

**Prompt for your LLM after editing this Markdown:**

> Read the attached Markdown. Regenerate the sibling `.json` so every field matches.
> Preserve the schema documented in the rows above.
> Output each file in its own fenced code block tagged with the filename.
> Output only the JSON object.

_This Markdown was auto-synthesized from the catalog row for `L1-078`._
_Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._