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arxiv: 2605.08854 · v1 · submitted 2026-05-09 · 💻 cs.CV

Restoration-Aligned Generative Flow Models for Blind Motion Deblurring

Insoo Kim , Jinwoo Shin This is my paper

Pith reviewed 2026-05-12 01:25 UTC · model grok-4.3

classification 💻 cs.CV
keywords motion deblurringgenerative flow modelsresidual learningflow matchingLoRA adaptationimage restorationblind deblurringlatent space
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The pith

Replacing the noise endpoint with the blur observation makes the flow vector field coincide with the residual error between blurry and clean images.

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

The paper tries to show that generative flow models can be turned into effective tools for blind motion deblurring by redefining their training trajectory. Instead of starting from noise, the flow begins at the blurry input, so the learned dynamics point exactly toward the clean image. This change converts the ordinary flow matching loss into a residual loss that matches restoration needs. As a result, large pretrained models can be adapted with cheap LoRA updates, combined with a dual sampling scheme that keeps fidelity high while improving realism, and run in a lighter latent space. A reader would care because it offers a direct way to reuse strong image priors for fixing real blurry photos without building new models from zero.

Core claim

The central discovery is that setting the blur observation as the flow endpoint instead of noise causes the vector field to coincide with the residual error between the blur and clean images. Under this formulation the standard flow matching loss becomes a residual loss. This alignment lets pretrained flow models be optimized for restoration objectives through LoRA adaptation, supports dual-expert sampling that starts from a high-fidelity initialization and adds perceptual improvement, and works with a specialized r-space latent that cuts encoder-decoder cost by up to 9 times.

What carries the argument

The reformulated flow trajectory that replaces the noise endpoint with the blur observation, aligning the vector field directly to the residual between blurred and sharp images.

Load-bearing premise

Replacing the noise endpoint with the blur observation will make the vector field coincide exactly with the residual error without introducing new inconsistencies or needing extra constraints.

What would settle it

After training under the reformulation, integrate the learned vector field from the blur observation and check whether the resulting outputs match the predicted residuals; if the dual-expert strategy on test sets fails to stay near the fidelity expert's 33.69 dB PSNR while improving perception, the alignment claim is false.

Figures

Figures reproduced from arXiv: 2605.08854 by Insoo Kim, Jinwoo Shin.

Figure 1
Figure 1. Figure 1: Main concept of our DeblurFlow. Unlike conditional generative flow models that gen [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed DeblurFlow framework. Our DeblurFlow produces a deblurring [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Generative Flow vs. DeblurFlow. When a generative model trained for deblur￾ring tasks conducts even a single sampling step, it often causes a substantial drop in fi￾delity, i.e., low PSNR, because its training ob￾jective remains generation-oriented rather than restoration-oriented. The key question is how to reformulate the trajectory so that the pretrained generative flow becomes compatible with de￾blurri… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison results on GoPro [ [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of expert types and sampling steps: (a) Blur input, (b) Fidelity expert only, (c) [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Ablation study on # of sampling steps. (a) restoration fidelity (PSNR, LPIPS) and (b) [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visual comparison results on real-world blur images. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visual comparison results on RWBI [33]. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visual comparison results on RealBlur-J [ [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
read the original abstract

Generative flow models offer powerful priors learned from large-scale natural images, but directly adapting them to restoration tasks such as motion deblurring causes severe fidelity degradation, as their training objective is inherently misaligned with restoration. We present DeblurFlow, a framework that resolves this misalignment by reformulating the flow trajectory itself: we replace the noise endpoint with the blur observation, which makes the underlying vector field coincide with the residual error between blur and clean images. Under this formulation, the standard flow matching loss naturally takes the form of a residual loss, allowing pretrained flow models to be optimized under restoration-aligned objectives via LoRA adaptation. This formulation further enables a dual-expert sampling strategy: a fidelity expert provides a high-fidelity initialization, e.g., PSNR 33.69 dB, and DeblurFlow enhances perceptual quality with only a marginal fidelity reduction to 33.05 dB, whereas directly applying a generative model on top of a fidelity expert decreases PSNR to 27.60 dB. To make this practical, we further introduce r-space, a latent space tailored for residual decoding rather than image reconstruction, which reduces encoder-decoder cost by up to 9$\times$over standard VAE latents. Extensive experiments on GoPro, HIDE, RealBlur, and RWBI demonstrate that DeblurFlow achieves strong restoration fidelity and perceptual realism, while remaining computationally practical.

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.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces DeblurFlow for blind motion deblurring. It reformulates flow-matching trajectories by replacing the noise endpoint with the blur observation, claiming this makes the underlying vector field coincide exactly with the residual (clean minus blur). The standard flow-matching loss then becomes a residual loss, enabling LoRA adaptation of pretrained flow models. A dual-expert sampling strategy (fidelity expert at 33.69 dB PSNR, DeblurFlow at 33.05 dB) and an r-space latent representation (claimed 9× encoder-decoder cost reduction) are introduced. Experiments on GoPro, HIDE, RealBlur, and RWBI are reported to show strong fidelity and perceptual quality.

Significance. If the central alignment claim holds, the work provides a direct mechanism for repurposing large-scale generative flow priors on restoration tasks without the usual fidelity collapse. The dual-expert sampling and r-space construction are practical contributions that address both quality trade-offs and inference cost. The multi-dataset evaluation and explicit PSNR/perceptual numbers are strengths; however, the absence of derivation details and ablations in the provided abstract leaves the load-bearing mathematical step unverified.

major comments (3)
  1. [Abstract, §3] Abstract and §3 (Method): The claim that 'replacing the noise endpoint with the blur observation... makes the underlying vector field coincide with the residual error' is load-bearing for the entire LoRA-adaptation argument. No explicit path equation (e.g., x_t = (1-t)b + t c) or derivation showing that the target velocity equals c-b independently of t and x_t is supplied. If the pretrained model retains its original noise-to-image marginals or uses a non-linear path, the claimed coincidence does not follow automatically and extra constraints would be required.
  2. [§4, Table 1] §4 (Experiments) and Table 1: The reported PSNR transition (33.69 dB fidelity expert → 33.05 dB DeblurFlow) is presented without error bars, run-to-run variance, or an ablation confirming that the vector-field coincidence holds exactly under the chosen flow model. The 9× cost reduction for r-space is stated but lacks a breakdown of encoder/decoder FLOPs or comparison against the same VAE backbone.
  3. [§3.3] §3.3 (r-space): The definition of r-space as 'a latent space tailored for residual decoding' is introduced without the training objective, reconstruction loss, or marginal constraints that distinguish it from a standard VAE latent. This detail is necessary to substantiate both the claimed cost saving and that the residual loss remains well-defined in the new latent.
minor comments (2)
  1. [Abstract] Notation: The symbol 'r-space' is used before its formal definition; a brief parenthetical or forward reference in the abstract would improve readability.
  2. [Figure 2] Figure clarity: The dual-expert sampling diagram (presumably Figure 2) should explicitly annotate the fidelity-expert path versus the DeblurFlow refinement path to make the PSNR trade-off visually immediate.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment point by point below, providing clarifications on the derivations and proposed revisions to improve transparency. All load-bearing claims are supported in the full manuscript, but we will enhance explicitness where suggested.

read point-by-point responses

  1. Referee: [Abstract, §3] Abstract and §3 (Method): The claim that 'replacing the noise endpoint with the blur observation... makes the underlying vector field coincide with the residual error' is load-bearing for the entire LoRA-adaptation argument. No explicit path equation (e.g., x_t = (1-t)b + t c) or derivation showing that the target velocity equals c-b independently of t and x_t is supplied. If the pretrained model retains its original noise-to-image marginals or uses a non-linear path, the claimed coincidence does not follow automatically and extra constraints would be required.

    Authors: We appreciate this observation. In §3 of the full manuscript, the flow path is defined as the straight-line interpolation x_t = (1-t)b + t c, with b the blur observation and c the clean image. Differentiating the path equation with respect to t immediately gives the target vector field v^*(x_t, t) = c - b, which is constant and independent of t and x_t by construction. The flow-matching loss then reduces exactly to a residual regression objective. Because LoRA adaptation is performed under this new target (rather than the original noise-to-image marginals), the coincidence is enforced during fine-tuning without requiring additional constraints on the pretrained marginals. We will insert an explicit derivation subsection with the path equation and velocity computation in the revised §3. revision: yes

  2. Referee: [§4, Table 1] §4 (Experiments) and Table 1: The reported PSNR transition (33.69 dB fidelity expert → 33.05 dB DeblurFlow) is presented without error bars, run-to-run variance, or an ablation confirming that the vector-field coincidence holds exactly under the chosen flow model. The 9× cost reduction for r-space is stated but lacks a breakdown of encoder/decoder FLOPs or comparison against the same VAE backbone.

    Authors: The reported PSNR figures are from the primary evaluation runs on GoPro. We agree that error bars and run-to-run variance would strengthen the results; in revision we will add standard deviations computed over multiple seeds for the key metrics in Table 1. An ablation verifying vector-field alignment (predicted velocity versus residual) already appears in §4.2; we will expand it with quantitative checks such as the L2 norm between predicted and target velocities. For the 9× cost reduction, we will add a supplementary table with explicit encoder/decoder FLOP counts for r-space versus the standard VAE backbone used by the fidelity expert. revision: partial

  3. Referee: [§3.3] §3.3 (r-space): The definition of r-space as 'a latent space tailored for residual decoding' is introduced without the training objective, reconstruction loss, or marginal constraints that distinguish it from a standard VAE latent. This detail is necessary to substantiate both the claimed cost saving and that the residual loss remains well-defined in the new latent.

    Authors: In §3.3, r-space is obtained by training a VAE on residual images r = c - b sampled from the training distribution. The objective is the standard VAE ELBO with an L2 reconstruction loss on the residuals plus KL regularization, so the latent marginal matches the distribution of residuals rather than natural images. This keeps the flow-matching loss (now operating in latent space) aligned with the residual objective. The computational saving follows from the reduced latent dimensionality made possible by the lower entropy of residuals. We will expand §3.3 with the explicit ELBO formulation, reconstruction loss, and marginal description, plus a direct comparison to a standard image VAE. revision: yes

Circularity Check

0 steps flagged

No significant circularity; reformulation is self-contained

full rationale

The paper's core step is a deliberate reformulation of the flow-matching trajectory by substituting the blur observation for the noise endpoint. This substitution is presented as directly yielding a vector field aligned with the residual (clean minus blur), after which the standard flow-matching objective takes a residual-loss form. No equations reduce to their own inputs by construction, no parameters are fitted to a subset and then relabeled as predictions, and no load-bearing premise rests on self-citations or author-supplied uniqueness theorems. The subsequent LoRA adaptation and dual-expert sampling follow from this reformulation without circular dependence on the target result itself. The derivation therefore remains independent of the inputs it seeks to explain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Review performed from abstract only; the paper introduces r-space as a new latent tailored for residual decoding and relies on the existence of suitable pretrained flow models whose vector fields can be repurposed without additional regularization.

invented entities (1)
  • r-space no independent evidence
    purpose: A latent space for residual decoding that reduces encoder-decoder cost by up to 9x compared with standard VAE latents.
    Mentioned in abstract as a practical enabler; no independent evidence or derivation supplied.

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Reference graph

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