L1 ⚛ L1-077 ⊙ Testnet

Coded-Exposure Imaging for Motion Deblur

Computational Photography · Temporal coding for motion robust imaging

⬇ Download bundle 🚀 Submit to mainnet — earn 100 PWM + 5% royalty

Markdown — human-readable source of truth

⚙ auto-generated

⬇ L1-077.md

# ⚛ L1 Principle — Coded-Exposure Imaging for Motion Deblur **ID:** `L1-077` · **Status:** ⊙ Testnet (genesis catalog) > **🌐 Domain:** Computational Photography — *Temporal coding for motion robust imaging* > **🎯 Problem class:** linear inverse · **🧮 Solution space:** 2D spatial > **📡 Carrier:** photon · **🌫 Noise:** gaussian > **⚖ Difficulty (δ):** 3 · **⛓ Block:** 41554170 --- ## 🧠 1. Introduction **Coded-Exposure Imaging for Motion Deblur** is a **linear inverse problem** whose unknown lives in **2D spatial** space, within the **Temporal coding for motion robust imaging** sub-domain of **Computational Photography**. Measurements consist of photons collected by an optical detector via a **coded temporal shutter** sensing mechanism. The forward operator applies, in order: S · temporal · coded operator; L · conv · motion operator; detector accumulates flux over the exposure window. Observations are corrupted by additive Gaussian noise. Coded kernel h_c is chosen to have approximately flat magnitude response (MURA or optimal binary codes); OTF is nonzero across full passband, so Wiener deconvolution is well-conditioned. Stability dominated by motion-direction / velocity mismatch and saturation clipping. ## ⚙ 2. Forward Model Physical chain: **x** → S · temporal · coded → L · conv · motion → Temporal integration → **y** (detector). ``` y = ∫_t dt `L.conv.motion` `S.temporal.coded` x + n, n ~ 𝒩(0, σ²) ``` **Measurement DAG:** | Primitive | What it does | |---|---| | `S.temporal.coded` | S · temporal · coded operator | | `L.conv.motion` | L · conv · motion operator | | `int.temporal` | Detector accumulates flux over the exposure window | ## 🔬 3. Physics Fingerprint | Property | Value | |---|---| | Domain | Computational Photography | | Sub domain | Temporal coding for motion robust imaging | | Carrier | photon | | Problem class | linear_inverse | | Solution space | 2D_spatial | | Noise model | gaussian | | Integration axis | temporal | | Difficulty delta | 3 | | L dag | 2.5 | ## 📡 4. Measurement Model Coded kernel h_c is chosen to have approximately flat magnitude response (MURA or optimal binary codes); OTF is nonzero across full passband, so Wiener deconvolution is well-conditioned. Stability dominated by motion-direction / velocity mismatch and saturation clipping. | Metric | Value | |---|---| | Metric | PSNR_dB | | Secondary | SSIM | ## 📏 5. Operating Range (Ω) **Center problem class:** `motion_deblur_coded_exposure` · **Forward operator:** `coded_exposure_convolution` **Center point:** | Parameter | Unit | Value | |---|---|---| | H | px | 1024 | | W | px | 1024 | | T ms | ms | 52 | | Velocity px | — | 20 | | Photon count | — | 1000 | | Code length bits | — | 52 | | Motion velocity error | — | 0 | | Motion direction error | — | 0 | **Allowed bounds:** | Parameter | Unit | Range | |---|---|---| | H | px | 256 – 4096 | | W | px | 256 – 4096 | | T ms | ms | 5 – 200 | | Velocity px | — | 1.0 – 100.0 | | Photon count | — | 50 – 10000 | | Saturation clip | — | 0.0 – 0.1 | | Code length bits | — | 16 – 128 | | Motion velocity error | — | 0.0 – 0.5 | | Motion direction error | — | 0.0 – 0.3 | | Exposure duration error | — | 0.0 – 0.2 | ## 🎯 6. Tolerance (ε) **Center tolerance:** 28.0 dB PSNR | Metric | Range | |---|---| | Psnr db | 18.0 – 40.0 | ## ⚖ 7. Hardness Function Hardness scales as **`epsilon_fn`** on **PSNR_dB**, with κ = `900` and δ = `3`. ## 💾 8. Reference Dataset - **primary** · weight 1.0 · IPFS _(not pinned yet)_ ## 9. On-chain Registration - **Chain hash:** `0xb6f8a13d8edc6c4a4df0bc75d347c52640a7a706eeee51de7bd9ef6840d3d440` - **Chain tx hash:** `0x672983fc701f89da4e626c4fdd69915f90d2fee45d9fc54c8edfb7de2b85914a` - **Chain block:** `41554170` --- ## File Mapping This bundle consists of: `L1-077.md`, `L1-077.json`. | File | Role | How to regenerate | |------|------|-------------------| | `L1-077.md` | Source of truth — edit this | Human or LLM | | `L1-077.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 `L1-077`._ _Edit it, regenerate the JSON, and submit at [/submit](/submit) to claim the artifact._

📝 Polish this artifact in your LLM

Download the files on the right.
Paste the Markdown into ChatGPT / Claude / Gemini and edit freely.
Send your edited Markdown back with the prompt below.
Save the regenerated JSON, then submit via /submit.

Prompt to give your LLM

Read the attached Markdown (L1-077.md).
Regenerate L1-077.json so every field matches the Markdown.
Preserve the schema documented in the File Mapping footer at the bottom of the MD.
Output each file in its own fenced code block tagged with the filename.

⌥ Hierarchy

Upstream lineage and downstream children

↓ L2 Spec 1 child ▾

L2-077-001 Coded-Exposure Mismatch-Only Spec

⬇ Download bundle

2 files · click to download

📋 JSON metadata

{
  "artifact_id": "L1-077",
  "chain_block": 41554170,
  "chain_hash": "0xb6f8a13d8edc6c4a4df0bc75d347c52640a7a706eeee51de7bd9ef6840d3d440",
  "chain_tx_hash": "0x672983fc701f89da4e626c4fdd69915f90d2fee45d9fc54c8edfb7de2b85914a",
  "domain": "Computational Photography",
  "hardness_fn": {
    "delta": 3,
    "kappa": 900,
    "metric": "PSNR_dB",
    "type": "epsilon_fn"
  },
  "initiator_dataset": [
    {
      "ipfs_cid": null,
      "license_hash": null,
      "name": "primary",
      "weight": 1.0
    }
  ],
  "layer": "L1",
  "observable_profile": {
    "metric": "PSNR_dB",
    "regime": "Coded kernel h_c is chosen to have approximately flat magnitude response (MURA or optimal binary codes); OTF is nonzero across full passband, so Wiener deconvolution is well-conditioned. Stability dominated by motion-direction / velocity mismatch and saturation clipping.",
    "secondary": "SSIM"
  },
  "physics_fingerprint": {
    "L_DAG": 2.5,
    "carrier": "photon",
    "difficulty_delta": 3,
    "domain": "Computational Photography",
    "integration_axis": "temporal",
    "noise_model": "gaussian",
    "primitives": [
      "S.temporal.coded",
      "L.conv.motion",
      "int.temporal"
    ],
    "problem_class": "linear_inverse",
    "sensing_mechanism": "coded_temporal_shutter",
    "solution_space": "2D_spatial",
    "sub_domain": "Temporal coding for motion robust imaging",
    "title": "Coded-Exposure Imaging for Motion Deblur"
  },
  "size_tiers": {
    "allowed_forward_operators": [
      "coded_exposure_convolution",
      "flutter_shutter",
      "coded_exposure_stereo"
    ],
    "allowed_omega_dimensions": [
      "H",
      "W",
      "T_ms",
      "code_length_bits",
      "velocity_px",
      "photon_count",
      "motion_velocity_error",
      "motion_direction_error",
      "exposure_duration_error",
      "saturation_clip"
    ],
    "allowed_problem_classes": [
      "motion_deblur_coded_exposure",
      "rolling_shutter_coded"
    ],
    "center_spec": {
      "epsilon_fn_center": "28.0 dB PSNR",
      "forward_operator": "coded_exposure_convolution",
      "input_format": "blurred_image_plus_code_plus_motion_hint",
      "omega": {
        "H": 1024,
        "T_ms": 52,
        "W": 1024,
        "code_length_bits": 52,
        "motion_direction_error": 0.0,
        "motion_velocity_error": 0.0,
        "photon_count": 1000,
        "velocity_px": 20.0
      },
      "problem_class": "motion_deblur_coded_exposure"
    },
    "epsilon_bounds": {
      "psnr_db": [
        18.0,
        40.0
      ]
    },
    "omega_bounds": {
      "H": [
        256,
        4096
      ],
      "T_ms": [
        5,
        200
      ],
      "W": [
        256,
        4096
      ],
      "code_length_bits": [
        16,
        128
      ],
      "exposure_duration_error": [
        0.0,
        0.2
      ],
      "motion_direction_error": [
        0.0,
        0.3
      ],
      "motion_velocity_error": [
        0.0,
        0.5
      ],
      "photon_count": [
        50,
        10000
      ],
      "saturation_clip": [
        0.0,
        0.1
      ],
      "velocity_px": [
        1.0,
        100.0
      ]
    }
  },
  "staked_pwm": 0.0,
  "status": "testnet",
  "sub_domain": "Temporal coding for motion robust imaging",
  "title": "Coded-Exposure Imaging for Motion Deblur"
}

🎁

Earn PWM by promoting this to mainnet

• 100 PWM one-time grant on mainnet promotion
• 5% upstream royalty on every downstream L4 mining event, forever
• Your wallet is enrolled the moment the founder accepts

🚀 Submit to mainnet →

How: Download the bundle above → polish the Markdown in ChatGPT / Claude / Gemini → submit your version → founder reviews → mainnet.