FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring

Jiezhang Cao; Xiaoyang Liu; Yulun Zhang; Zheng Chen; Zhengyan Zhou; Zihang Xu

arxiv: 2510.01641 · v3 · submitted 2025-10-02 · 💻 cs.CV

FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring

Xiaoyang Liu , Zhengyan Zhou , Zihang Xu , Jiezhang Cao , Zheng Chen , Yulun Zhang This is my paper

Pith reviewed 2026-05-18 11:05 UTC · model grok-4.3

classification 💻 cs.CV

keywords motion deblurringdiffusion modelsconsistency modelhigh-fidelity restorationsingle-step inferenceblur kernel estimationimage restorationtemporal consistency

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The pith

A consistency model trained on matched blur trajectories enables single-step high-fidelity motion deblurring.

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

The paper presents FideDiff as a way to harness large pre-trained diffusion models for motion deblurring without their usual drawbacks of slow multi-step inference and reduced detail. It reframes the task so that each timestep corresponds to a progressively blurred version of the same image, then trains a consistency model to map every timestep directly back to one clean output. Training data is built by applying blur trajectories that align exactly with those timesteps, so the model internalizes the mapping from blur to sharp without needing many denoising passes. Adding a Kernel ControlNet to estimate the blur kernel and an adaptive way to choose the timestep further refines the results. A reader would care if this holds because it points to a practical route for using the generative strength of diffusion models in everyday image repair tasks that demand both speed and accuracy.

Core claim

The paper claims that motion deblurring can be recast as a diffusion-like sequence in which each timestep stands for a more blurred image, and a consistency model can be trained to pull every timestep to the identical clean image. Reconstructing the training set with blur trajectories that match the timesteps lets the model acquire the necessary temporal consistency for accurate one-step restoration. Kernel ControlNet supplies blur kernel estimates while adaptive timestep prediction adjusts the process to the input, yielding full-reference metrics that exceed earlier diffusion approaches and equal other leading methods.

What carries the argument

The consistency model that aligns every timestep in a reformulated diffusion-like blur process to the same clean image, augmented by Kernel ControlNet for kernel estimation.

If this is right

One-step inference replaces multi-step sampling, cutting the computation needed for each deblurred output.
Full-reference metrics improve over prior diffusion deblurring methods while remaining competitive with non-diffusion state-of-the-art models.
Kernel ControlNet supplies explicit blur kernel information that guides the restoration process.
Adaptive timestep prediction lets the model adjust its starting point to the severity of blur in each input.

Where Pith is reading between the lines

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

The same matched-trajectory training idea could be tested on other restoration problems such as denoising or low-light enhancement to see if single-step consistency models generalize there.
Extending the approach to video sequences would require checking whether frame-to-frame temporal consistency holds when blur trajectories are applied across time.
Larger pre-trained diffusion backbones could be swapped in to test whether the fidelity gains scale with model size on the same deblurring benchmarks.

Load-bearing premise

Reconstructing training data with blur trajectories that match the model's timesteps will let the consistency model learn temporal consistency that generalizes to real motion blur without fidelity loss or artifacts.

What would settle it

Apply the trained model to a held-out set of real-world motion-blurred photographs captured outside controlled conditions and check whether full-reference scores such as PSNR or SSIM drop below those of multi-step diffusion baselines or whether visible artifacts appear relative to ground-truth sharp images.

read the original abstract

Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in real-world modeling, have shown great promise for high-quality image restoration tasks such as deblurring, demonstrating stronger generative capabilities than CNN and transformer-based methods. However, challenges such as unbearable inference time and compromised fidelity still limit the full potential of the diffusion models. To address this, we introduce FideDiff, a novel single-step diffusion model designed for high-fidelity deblurring. We reformulate motion deblurring as a diffusion-like process where each timestep represents a progressively blurred image, and we train a consistency model that aligns all timesteps to the same clean image. By reconstructing training data with matched blur trajectories, the model learns temporal consistency, enabling accurate one-step deblurring. We further enhance model performance by integrating Kernel ControlNet for blur kernel estimation and introducing adaptive timestep prediction. Our model achieves superior performance on full-reference metrics, surpassing previous diffusion-based methods and matching the performance of other state-of-the-art models. FideDiff offers a new direction for applying pre-trained diffusion models to high-fidelity image restoration tasks, establishing a robust baseline for further advancing diffusion models in real-world industrial applications. Our dataset and code will be available at https://github.com/xyLiu339/FideDiff.

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

FideDiff turns a pre-trained diffusion model into a one-step deblurrer via consistency training on matched synthetic blur trajectories plus Kernel ControlNet, but the synthetic-to-real gap is the part that needs checking.

read the letter

The main point is a consistency-model reformulation that treats motion deblurring as a diffusion-like process where timesteps stand for increasing blur levels. They build training data by applying blur kernels along trajectories chosen to line up with the timestep schedule, then train so the model maps any timestep straight back to the clean image. Kernel ControlNet is added for kernel estimation and they include adaptive timestep prediction to improve the one-step output. This directly targets the slow sampling that has kept diffusion models from being practical for restoration work.

Referee Report

1 major / 1 minor

Summary. The paper introduces FideDiff, a single-step diffusion model for high-fidelity motion deblurring. It reformulates deblurring as a diffusion-like process with timesteps as progressively blurred images, trains a consistency model to map all timesteps to the clean image, reconstructs training data using matched blur trajectories to learn temporal consistency, and augments the model with Kernel ControlNet for kernel estimation plus adaptive timestep prediction. The central claim is superior full-reference metrics over prior diffusion methods while matching other state-of-the-art approaches.

Significance. If the performance claims hold, the work is significant for showing how consistency models can be derived from pre-trained diffusion models to achieve efficient, high-fidelity restoration, directly addressing inference-time and fidelity limitations. The planned public release of dataset and code is a clear strength for reproducibility.

major comments (1)

[Abstract] Abstract: the headline claim of superior full-reference metrics and successful one-step inference depends on the assumption that training-data reconstruction with matched blur trajectories produces a distribution statistically close enough to real camera motion blur for the consistency mapping to generalize without fidelity loss or artifacts. No quantitative support (distribution distances, trajectory-mismatch ablations, or real-vs-synthetic error analysis) is supplied to substantiate that the domain gap is negligible.

minor comments (1)

The phrase 'Kernel ControlNet' is presented without a reference or architectural diagram; a short comparison to standard ControlNet and a citation to its source would improve clarity for readers.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment point by point below.

read point-by-point responses

Referee: [Abstract] Abstract: the headline claim of superior full-reference metrics and successful one-step inference depends on the assumption that training-data reconstruction with matched blur trajectories produces a distribution statistically close enough to real camera motion blur for the consistency mapping to generalize without fidelity loss or artifacts. No quantitative support (distribution distances, trajectory-mismatch ablations, or real-vs-synthetic error analysis) is supplied to substantiate that the domain gap is negligible.

Authors: We appreciate the referee's careful reading and the identification of this potential limitation. The use of matched blur trajectories in data reconstruction is a deliberate design choice to ensure that the synthetic training distribution follows the same progressive blurring process as the diffusion-like reformulation, thereby enabling the consistency model to learn temporal consistency without introducing extraneous variations. While the current version of the manuscript does not include explicit quantitative measures such as distribution distances, trajectory-mismatch ablations, or formal real-versus-synthetic error analysis, the reported results on both synthetic benchmarks and real-world images (including superior full-reference metrics relative to prior diffusion approaches) provide indirect evidence that any domain gap does not materially degrade fidelity or introduce artifacts. To directly address the concern, we will expand the revised manuscript with additional discussion of the trajectory-matching rationale and, where feasible, supplementary qualitative or statistical comparisons between the reconstructed data and real camera blur distributions. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper presents a new single-step consistency model for deblurring by reformulating the task as a diffusion-like process and training on synthetically reconstructed blur trajectories. Performance is reported via standard full-reference metrics on external benchmarks, with no equations or claims showing that any prediction reduces by construction to a fitted input, self-defined quantity, or load-bearing self-citation. The central method (Kernel ControlNet integration and adaptive timestep prediction) is described as an architectural choice with empirical validation rather than a tautological redefinition of inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are detailed beyond the high-level description of the consistency model and ControlNet module.

invented entities (1)

Kernel ControlNet no independent evidence
purpose: Blur kernel estimation to enhance deblurring performance
Presented as an integrated component; no independent evidence or falsifiable prediction outside the model is mentioned.

pith-pipeline@v0.9.0 · 5793 in / 1061 out tokens · 32053 ms · 2026-05-18T11:05:08.923036+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 reformulate motion deblurring as a diffusion-like process where each timestep represents a progressively blurred image, and we train a consistency model that aligns all timesteps to the same clean image. By reconstructing training data with matched blur trajectories...
IndisputableMonolith/Foundation/DimensionForcing.lean alexander_duality_circle_linking unclear

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

we define the forward process through blur trajectories... q(k1:T |k0) = product q(kt|kt-1:0)

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Forward citations

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