L1 ⚛ L1-077 ⊙ Testnet

Coded-Exposure Imaging for Motion Deblur

Computational Photography · Temporal coding for motion robust imaging

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⬇ principle.md ⬇ principle.json

Fork L1-077

⬇ L1-077.md ⬇ L1-077.json

Prompt — copy into your LLM

You are helping me submit a MODIFICATION of L1-077 (Coded-Exposure Imaging for Motion Deblur) to the PWM Protocol — a Principle (L1) artifact.

I will paste a Markdown template (or the current L1-077.md).
1. Rewrite the Markdown so the science is correct and clearly explained for my change.
2. Regenerate the sibling L1-077.json so EVERY field matches the Markdown.
3. Keep the schema in the "File Mapping" footer at the bottom of the MD.
4. Keep the parent reference unchanged unless I ask otherwise.
Rules: the Markdown is the source of truth; use SI units; do NOT invent benchmark scores.
Output each file in its own fenced code block tagged with the filename.

Here is my template:
[PASTE THE .md HERE]

⚙ Forward Model

y = ∫_t dt `L.conv.motion` `S.temporal.coded` x + n,    n ~ 𝒩(0, σ²)

world state x (2D spatial)

→

S · temporal · coded `S.temporal.coded`

→

L · conv · motion `L.conv.motion`

→

Temporal integration ∫_t dt

→

observation y (photon detector)

Noise: additive Gaussian noise

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._

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📋 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"
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    "allowed_omega_dimensions": [
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      "T_ms",
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      "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": [
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        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"
}

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