Writing study: Diffusion Image Prior

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← ICCV 2025 oral writing corpus

Hamadi Chihaoui, Paolo Favaro · Official ICCV 2025 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Empirical analysis, diagnosis & auditing (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF

9 physical pages

Before references / appendix

≈ 8.05 page-equivalents

Main figures / tables

7 / 6

Attached appendix figures / tables

0 / 0

Attached appendix span

No boundary detected; not assumed zero

Before references / appendix: ≈ 89.44%References / intervening material: ≈ 10.56%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Extraction notes: No attached appendix boundary detected. Zero means no recognized caption, not independently verified absence.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level heading	Starts on PDF page	Estimated span	Share of entire PDF
1. Introduction	1	≈ 1.07 pp.	11.89%
2. Related Work	2	≈ 0.85 pp.	9.44%
3. The DIffusion Image Prior	3	≈ 3.48 pp.	38.67%
4. Experiments	6	≈ 1.06 pp.	11.78%
5. Ablations	7	≈ 0.56 pp.	6.22%
6. Conclusion	8	≈ 0.69 pp.	7.67%
References	9	≈ 0.95 pp.	10.56%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 3
• Figure 3 · p. 4
• Figure 4 · p. 5
• Table 1 · p. 6
• Table 2 · p. 6
• Table 3 · p. 6
• Figure 5 · p. 7
• Figure 6 · p. 7
• Table 4 · p. 8
• Table 5 · p. 8
• Table 6 · p. 8
• Figure 7 · p. 8

Attached appendix

No recognized caption labels.

Argument and organization

1. 1 Introduction; 2 Related Work

   Contrasts fully blind restoration with methods requiring a parametric degradation model.

2. 3 The DIffusion Image Prior

   Revisits DIP, studies frozen-model latent reconstruction and derives observable early-stopping rules.

3. 4 Experiments; 5 Ablations; 6 Conclusion

   Separates structured/unstructured tasks, stopping-rule sensitivity, oracle gap and computational cost.

Problem, claim and contribution

Two explicit questions ask whether a pretrained diffusion model has a reconstruction prior and how it differs from an untrained DIP network.

Section 3.3, physical pp.3–4 · Main paper

Argument and section order

The paper moves from optimization trajectories to stopping criteria before presenting the final algorithm, so the rule follows an observed failure mode.

Sections 3.3–3.5, pp.4–6 · Main paper

Figures, captions and visual explanation

The paired trajectory plots compare an observable normalized loss slope against clean-reference PSNR; these are different information sources rather than two deployable signals.

Figure 4, p.5 · Main paper

Tables, comparisons and evidence

Main tables separate partially/fully blind applicability, fidelity and perceptual metrics, and cost; water-drop LPIPS and 4× super-resolution retain baseline wins.

Tables 4–6, p.8 · Main paper

Appendix and reproducibility

The detailed latent-optimization algorithm is assigned to Section 1 of the supplementary material, which was not inspected.

Section 3.3, p.4 · Main paper

Clarity and writing conventions

A dedicated oracle-stopping comparison makes the remaining gap explicit rather than describing early stopping as guaranteed optimal.

Table 3, p.6; Section 5, pp.7–8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Explain a stopping rule with paired observable and oracle trajectories, then quantify both its parameter sensitivity and its gap to the oracle.

Boundary: Fully blind and training-free describe test-time degradation knowledge and frozen weights, not absence of clean-data pretraining or iterative optimization. Generative plausibility does not establish recovered factual detail. Table 3’s water-drop value differs from Tables 1/5, so avoid combining those numbers as one matched result; the empirical stopping behavior is not a theorem for arbitrary corruption.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-10-07

Reading status

Writing-focused manuscript scan + selected visuals inspected

Main text

Writing-oriented scan across the main manuscript, physical PDF pp.1–8, covering framing, section progression, method explanations, captions, comparison protocols and concluding scope; reference boundary checked. Selected full-page rasters were inspected. Not exhaustive line-by-line reading, proof verification or experimental reproduction.

Appendix / supplement

Main-paper appendix/supplement references were checked. A separate supplementary source is linked in the inventory but was not retrieved or inspected; its contents are not included in the reading or visual-page counts.

Visually inspected PDF pages

5, 8

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.

• Official CVF open-access ICCV 2025 accepted-paper PDF, retrieved 7 October 2026; CVF states identical to accepted version except watermark; not assumed identical to initial submission · 9 pages