Writing study: Diffusion Transformer meets Multi-level Wavelet Spectrum for Single Image Super-Resolution

ICCV overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICCV 2025 oral writing corpus

Peng Du, Hui Li, Han Xu, Paul Barom Jeon, Dongwook Lee, Daehyun Ji, Ran Yang, Feng Zhu · 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: Architecture & representation (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

11 physical pages

Before references / appendix

≈ 8.05 page-equivalents

Main figures / tables

8 / 7

Attached appendix figures / tables

0 / 0

Attached appendix span

No boundary detected; not assumed zero

Before references / appendix: ≈ 73.18%References / intervening material: ≈ 26.82%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	≈ 1.39 pp.	12.64%
2. Related work	2	≈ 0.51 pp.	4.64%
3. Discrete wavelet transform	3	≈ 0.31 pp.	2.82%
4. Methodology	3	≈ 2.46 pp.	22.36%
5. Experiments	6	≈ 2.76 pp.	25.09%
6. Conclusion	8	≈ 0.24 pp.	2.18%
References	9	≈ 2.95 pp.	26.82%

All detected figure and table locations

Main text

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

Attached appendix

No recognized caption labels.

Argument and organization

1. 1 Introduction; 2 Related Work; 3 Discrete Wavelet Transform

   Motivates cross-scale frequency consistency and supplies the decomposition vocabulary.

2. 4 Methodology

   Separates wavelet diffusion, pyramid tokenization, low/high-frequency decoders and adversarial/reconstruction training.

3. 5 Experiments; 6 Conclusion

   Treats face, general-scene and real-degradation restoration separately before targeted frequency/decoder ablations.

Problem, claim and contribution

The claimed gap is dependence among frequency sub-bands, not simply the presence of a wavelet transform in a super-resolution network.

Sections 1–2, physical pp.1–3 · Main paper

Argument and section order

The paper introduces DWT before the model and assigns distinct roles to low-frequency elementary content and its high-frequency residual.

Sections 3–4.2, pp.3–5 · Main paper

Figures, captions and visual explanation

A sampling timeline is separated from a single denoising-step expansion; the tokenizer drawing aligns receptive fields and four-coordinate positional labels across scales.

Figures 2–3, p.4 · Main paper

Tables, comparisons and evidence

The comparison explicitly bolds best values among generative methods and retains stronger PSNR/SSIM regression baselines; real-world restoration uses a separately trained degradation setup.

Tables 2–5 and Section 5.2, p.7 · Main paper

Appendix and reproducibility

Additional hyperparameters and training details are routed to the supplement; those were not inspected.

Section 5.1, p.6 · Main paper

Clarity and writing conventions

The main discussion acknowledges the perception–distortion tradeoff, while the real-restoration result is phrased as winning most rather than every no-reference metric.

Sections 5.2–5.3, pp.6–8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Use subset-qualified highlighting and separate distortion, perceptual quality and downstream identity claims in restoration tables.

Boundary: Natural-looking high-frequency details are generated, not certified recovered facts. The model does not lead every listed metric; face authenticity language goes beyond image-similarity scores, and the real-world task uses a different training/degradation protocol from the synthetic SR comparisons.

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

4, 7

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 · 11 pages