SelfHVD: Self-Supervised Handheld Video Deblurring

Honglei Xu; Junjie Fan; Wangmeng Zuo; Xiaohe Wu; Zhilu Zhang

arxiv: 2508.08605 · v2 · submitted 2025-08-12 · 💻 cs.CV

SelfHVD: Self-Supervised Handheld Video Deblurring

Honglei Xu , Zhilu Zhang , Junjie Fan , Xiaohe Wu , Wangmeng Zuo This is my paper

Pith reviewed 2026-05-19 00:04 UTC · model grok-4.3

classification 💻 cs.CV

keywords video deblurringself-supervised learninghandheld videoimage restorationdeep learningcomputer visionmotion blur

0 comments

The pith

A self-supervised method deblurs handheld videos by using sharp clues from the input as labels for blurry neighbors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a self-supervised deblurring approach specifically for videos captured with unstable handheld devices. It identifies sharp clues within the video and treats them as misalignment labels to train the model on adjacent blurry frames. This avoids reliance on external paired sharp-blurry data and addresses the domain gap that limits supervised techniques on real footage. Additional components generate higher-quality training pairs internally and enforce consistency to avoid spatial shifts in the output. The resulting model shows stronger results than prior self-supervised alternatives on both custom and public real-world datasets.

Core claim

The method trains a deblurring network by extracting sharp clues directly from the handheld video and using them as misalignment labels for neighboring blurry frames, while a Self-Enhanced Video Deblurring process creates improved paired data and a Self-Constrained Spatial Consistency Maintenance regularizer prevents position shifts between input and output frames.

What carries the argument

Sharp clues extracted from the video itself, used as misalignment labels to supervise training of the deblurring model on neighboring frames.

If this is right

Training no longer requires separate collections of paired sharp and blurry videos.
Deblurred outputs maintain better spatial alignment with the original input frames.
The approach can be applied directly to real handheld footage without domain adaptation steps.
Performance gains hold across both synthetic handheld data and other common real-world blur datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same sharp-clue labeling idea could transfer to related tasks such as video denoising or super-resolution where partial clean signals exist in the input.
If the labeling step proves robust, it reduces dependence on large curated paired datasets for many video restoration problems.
Extending the consistency regularizer to longer temporal windows might further stabilize results on extended shaky sequences.

Load-bearing premise

Sharp clues present in the video can be reliably detected and applied as accurate labels for misalignment without introducing systematic errors into the training process.

What would settle it

Running the trained model on a handheld video sequence that contains no frames sharp enough to serve as reliable clues, then checking whether deblurring quality remains comparable to or worse than simpler baselines.

read the original abstract

Shooting video with handheld shooting devices often results in blurry frames due to shaking hands and other instability factors. Although previous video deblurring methods have achieved impressive progress, they still struggle to perform satisfactorily on real-world handheld video due to the blur domain gap between training and testing data. To address the issue, we propose a self-supervised method for handheld video deblurring, which is driven by sharp clues in the video. First, to train the deblurring model, we extract the sharp clues from the video and take them as misalignment labels of neighboring blurry frames. Second, to improve the deblurring ability of the model, we propose a novel Self-Enhanced Video Deblurring (SEVD) method to create higher-quality paired video data. Third, we propose a Self-Constrained Spatial Consistency Maintenance (SCSCM) method to regularize the model, preventing position shifts between the output and input frames. Moreover, we construct synthetic and real-world handheld video datasets for handheld video deblurring. Extensive experiments on these and other common real-world datasets demonstrate that our method significantly outperforms existing self-supervised ones. The code and datasets are publicly available at https://cshonglei.github.io/SelfHVD.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Self-supervised handheld deblurring that extracts sharp clues from the input video itself to label neighbors, plus SEVD and SCSCM modules and new datasets, but the clue extraction step carries a real risk of feeding back correlated errors.

read the letter

This paper gives a self-supervised way to deblur handheld videos by pulling sharp clues out of the video itself to label blurry neighbors, then layering on SEVD for better pairs and SCSCM to hold spatial consistency. They also release new datasets. It does a solid job focusing on the practical problem of real consumer footage rather than just synthetic cases. The self-supervised angle avoids needing external ground truth, which is a plus for this domain, and the consistency term addresses a common artifact in deblurring outputs. Public code and data are welcome additions. Where it could be softer is the assumption that sharp clues can be extracted reliably enough to act as misalignment labels. If the detection misses cases with subtle motion, errors could feed back into the model through the self-enhanced pairs. Since the datasets follow the same extraction process, this creates a somewhat closed system that might overstate gains compared to truly independent tests. The abstract does not provide much on ablations for this part. This work suits people in video restoration who deal with mobile or handheld capture. Readers interested in self-supervised methods for low-level vision tasks would find the technical pieces and the data release useful. I would send it to peer review. It tackles a relevant issue with a clear pipeline and enough novelty in the combination of ideas to warrant feedback from experts in the area.

Referee Report

2 major / 1 minor

Summary. The paper proposes SelfHVD, a self-supervised method for handheld video deblurring. It extracts sharp clues from the input video to serve as misalignment labels for neighboring blurry frames, introduces Self-Enhanced Video Deblurring (SEVD) to generate higher-quality paired training data, and Self-Constrained Spatial Consistency Maintenance (SCSCM) to regularize against position shifts. The authors construct new synthetic and real-world handheld video datasets and report that the method significantly outperforms prior self-supervised approaches on these datasets as well as other common real-world benchmarks.

Significance. If the central claims hold, the work would be significant for tackling the domain gap between synthetic training data and real handheld video blur without requiring paired supervision. The public release of code and datasets is a clear strength that supports reproducibility and future research in self-supervised video restoration.

major comments (2)

[Abstract and §3] Abstract and §3: The central self-supervised loop depends on extracting sharp clues from the same video to label misalignments in neighboring frames. The manuscript must provide a precise description of the extraction heuristic together with quantitative evidence (e.g., sharpness metrics or alignment statistics on held-out frames) that the selected clues are sufficiently sharp and spatially aligned to avoid introducing correlated errors that propagate through SEVD and SCSCM training.
[Dataset construction and evaluation sections] Dataset construction and evaluation sections: Because both the synthetic and real-world handheld datasets are built using the same clue-extraction procedure that drives training, the reported gains risk being partly circular. An external validation set (e.g., existing paired deblurring benchmarks with independent ground truth) should be used to demonstrate that performance improvements generalize beyond the self-supervised construction loop.

minor comments (1)

[Abstract] The abstract would be clearer if it briefly named the specific common real-world datasets used for comparison and the quantitative metrics reported.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3: The central self-supervised loop depends on extracting sharp clues from the same video to label misalignments in neighboring frames. The manuscript must provide a precise description of the extraction heuristic together with quantitative evidence (e.g., sharpness metrics or alignment statistics on held-out frames) that the selected clues are sufficiently sharp and spatially aligned to avoid introducing correlated errors that propagate through SEVD and SCSCM training.

Authors: We agree that a more detailed description is needed. In the revised manuscript, we will expand Section 3 with a precise algorithmic description of the sharp clue extraction heuristic. We will also add quantitative validation, including sharpness metrics (e.g., Laplacian variance) and alignment error statistics computed on held-out frames, to demonstrate that the selected clues are sufficiently sharp and spatially consistent. These additions will directly address concerns about potential error propagation through SEVD and SCSCM. revision: yes
Referee: [Dataset construction and evaluation sections] Dataset construction and evaluation sections: Because both the synthetic and real-world handheld datasets are built using the same clue-extraction procedure that drives training, the reported gains risk being partly circular. An external validation set (e.g., existing paired deblurring benchmarks with independent ground truth) should be used to demonstrate that performance improvements generalize beyond the self-supervised construction loop.

Authors: We acknowledge the risk of circularity for the newly constructed datasets. However, the manuscript already reports results on multiple independent common real-world deblurring benchmarks that were not built with our clue-extraction procedure. These external evaluations show consistent gains, supporting generalization. In the revision we will add an explicit discussion clarifying the independence of these benchmarks and, where feasible, include additional comparisons on existing paired synthetic benchmarks with ground-truth to further mitigate the concern. revision: partial

Circularity Check

1 steps flagged

Self-supervised sharp-clue extraction introduces moderate self-referential label dependency

specific steps

self definitional [Abstract]
"First, to train the deblurring model, we extract the sharp clues from the video and take them as misalignment labels of neighboring blurry frames."

Misalignment labels for training are generated by extracting sharp clues from the same video whose blurry frames are being deblurred; the resulting supervision signal is therefore defined in terms of content already present in the input, with no external ground-truth reference to break the dependency.

full rationale

The core training signal derives misalignment labels directly from sharp clues extracted from the identical input video frames used for deblurring, creating a closed loop in pseudo-label generation. However, the paper constructs separate synthetic and real-world datasets, reports external comparisons to prior self-supervised baselines, and adds independent regularization via SCSCM, preventing the central claim from reducing fully to its inputs by construction. No equations or self-citations are shown to force equivalence, so circularity remains limited rather than dominant.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the assumption that sharp regions can be extracted reliably from blurry video and that self-generated pairs improve generalization; no explicit free parameters or invented physical entities are described in the abstract.

axioms (1)

domain assumption Sharp clues extracted from the video serve as accurate misalignment labels for neighboring frames.
Invoked in the first step of the method to drive training without external supervision.

pith-pipeline@v0.9.0 · 5756 in / 1228 out tokens · 32355 ms · 2026-05-19T00:04:20.095768+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

we extract the sharp clues from the video and take them as misalignment labels of neighboring blurry frames... Self-Enhanced Video Deblurring (SEVD)... Self-Constrained Spatial Consistency Maintenance (SCSCM)
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

vl(I) = E[(ΔI − ΔI)²]... Otsu’s method... optical flow... Lrec = 1/N Σ ||Mi ⊙ (Ri − Sj→i)||1

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