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arxiv: 2605.14267 · v1 · submitted 2026-05-14 · 💻 cs.CV · cs.AI

Recognition: 3 theorem links

· Lean Theorem

Image Restoration via Diffusion Models with Dynamic Resolution

Yang Zheng , Wen Li , Zhaoqiang Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-15 02:50 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords image restorationdiffusion modelsdynamic resolutionsubspace projectioncomputational efficiencyDPSDAPS
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The pith

Dynamic resolution diffusion models project images into lower-dimensional subspaces to speed up restoration.

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

The paper seeks to reduce the heavy computation of diffusion models for image restoration, which normally run in full pixel space or require repeated autoencoder passes in latent space. It does so by fine-tuning pre-trained models on dynamic resolution priors, then adapting two standard pixel-space restoration techniques into SubDPS and SubDAPS, with a further improved version called SubDAPS++. If the approach works, it delivers faster inference while keeping or improving reconstruction quality on a range of datasets and tasks.

Core claim

By fine-tuning pre-trained diffusion models for dynamic resolution priors, the work projects restoration problems into lower-dimensional subspaces, adapting DPS and DAPS to create SubDPS and SubDAPS, with SubDAPS++ further enhancing both speed and fidelity over recent diffusion-based approaches in most tested scenarios.

What carries the argument

Dynamic resolution priors from fine-tuned pre-trained diffusion models, which support subspace projection and the adapted restoration procedures SubDPS and SubDAPS.

If this is right

  • SubDAPS and SubDAPS++ achieve faster inference than pixel-space methods while avoiding the extra encoder-decoder cost of latent diffusion approaches.
  • The methods outperform recent DM-based approaches across the majority of tested datasets and restoration tasks.
  • SubDAPS++ adds further gains in both efficiency and reconstruction quality over the base SubDAPS version.
  • The framework applies to diverse image restoration problems without requiring full high-dimensional sampling at every step.

Where Pith is reading between the lines

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

  • The subspace idea could transfer to other diffusion-based generative tasks such as editing or synthesis where speed matters.
  • Reduced compute opens the door to running these models on edge hardware for near-real-time restoration.
  • Combining dynamic resolution with other accelerations like sampling shortcuts might compound the efficiency gains.
  • The method may scale to video restoration if temporal consistency can be maintained in the lower-dimensional space.

Load-bearing premise

Fine-tuning pre-trained diffusion models for dynamic resolution priors preserves sufficient information in the lower-dimensional subspaces to maintain reconstruction fidelity without introducing new artifacts.

What would settle it

Direct measurements showing that SubDAPS or SubDAPS++ produces lower PSNR, SSIM, or visibly worse details than standard DPS on the same deblurring or denoising benchmarks would disprove the claim of maintained or improved fidelity.

Figures

Figures reproduced from arXiv: 2605.14267 by Wen Li, Yang Zheng, Zhaoqiang Liu.

Figure 1
Figure 1. Figure 1: Overview of the proposed SubDAPS++ method. At each timestep ti, the method utilizes the data prediction network xθ(·, ·) to compute the unconditional estimate xˆ0. Subsequently, the conjugate gradient method aligns xˆ0 with the measurement y, yielding the conditional estimate x˜0. If the dimensionality changes at timestep ti, the algorithm upsamples x˜0 and injects random noise to match the diffusion prior… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization results of our proposed SubDAPS++ method and other baseline methods for the motion deblurring task, with Gaussian noise (σ = 0.05). 5. Conclusion In this work, we first fine-tune existing pre-trained DMs in pixel space to provide dynamic resolution priors and adapt two full-dimensional DM-based methods, namely DPS and DAPS, to dynamic resolution DMs, resulting in the SubDPS and SubDAPS method… view at source ↗
Figure 3
Figure 3. Figure 3: Detailed architectural diagram of the proposed SubDAPS++ framework, illustrating how the dynamic resolution prior is integrated into the diffusion sampling loop. Starting from a randomly initialized noisy vector xtN , the restoration process begins in a lower-dimensional subspace and progressively increases the dimensionality throughout sampling. At each resolution, the model first produces an unconditiona… view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of the experimental results for the inpainting (random 70%) task under Gaussian noise with a noise level of σ = 0.05. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of the experimental results for the super-resolution (4×) task under Gaussian noise with a noise level of σ = 0.05. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of the experimental results for the Gaussian deblurring task under Gaussian noise with a noise level of σ = 0.05. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of the experimental results for the motion deblurring task under Gaussian noise with a noise level of σ = 0.05. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visualization of the experimental results for the nonlinear deblurring task under Gaussian noise with a noise level of σ = 0.05. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualization of the experimental results for the high dynamic range recovery task under Gaussian noise with a noise level of σ = 0.05. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
read the original abstract

Diffusion models (DMs) have exhibited remarkable efficacy in various image restoration tasks. However, existing approaches typically operate within the high-dimensional pixel space, resulting in high computational overhead. While methods based on latent DMs seek to alleviate this issue by utilizing the compressed latent space of a variational autoencoder, they require repeated encoder-decoder inference. This introduces significant additional computational burdens, often resulting in runtime performance that is even inferior to that of their pixel-space counterparts. To mitigate the computational inefficiency, this work proposes projecting data into lower-dimensional subspaces using dynamic resolution DMs to accelerate the inference process. We first fine-tune pre-trained DMs for dynamic resolution priors and adapt DPS and DAPS, which are two widely used pixel-space methods for general image restoration tasks, into the proposed framework, yielding methods we refer to as SubDPS and SubDAPS, respectively. Given the favorable inference speed and reconstruction fidelity of SubDAPS, we introduce an enhanced variant termed SubDAPS++ to further boost both reconstruction efficiency and quality. Empirical evaluations across diverse image datasets and various restoration tasks demonstrate that the proposed methods outperform recent DM-based approaches in the majority of experimental scenarios. The code is available at https://github.com/StarNextDay/SubDAPS.git.

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

2 major / 2 minor

Summary. The manuscript proposes projecting images into lower-dimensional subspaces via dynamic resolution diffusion models to accelerate inference for image restoration tasks. Pre-trained DMs are fine-tuned to learn dynamic resolution priors; DPS and DAPS are then adapted into SubDPS and SubDAPS (with an enhanced SubDAPS++ variant). Experiments across multiple datasets and restoration tasks are reported to show outperformance versus recent DM-based methods in the majority of scenarios, with code released.

Significance. If the empirical gains hold after proper validation, the work would provide a practical route to lower the computational cost of diffusion-based restoration while preserving quality, addressing a clear limitation of pixel-space DMs. The public code release is a positive factor for reproducibility.

major comments (2)
  1. [Abstract and §5] Abstract and §5 (Experiments): the central claim of outperformance 'in the majority of experimental scenarios' lacks reported error bars, statistical significance tests, or ablations isolating the dynamic-resolution component from other implementation choices; without these the evidence remains moderate and the claim is not yet load-bearing.
  2. [§3] §3 (Method, dynamic-resolution fine-tuning and projection): no quantitative analysis, bounds, or ablation is given on information retention or high-frequency loss in the subspace projection after fine-tuning; this assumption is load-bearing for the fidelity claim yet untested.
minor comments (2)
  1. [§3] Notation for SubDAPS++ could be introduced more explicitly when first defined.
  2. [§5] Table captions should state the exact metrics and number of runs used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments highlight important aspects for strengthening the empirical support and methodological analysis, and we will revise the manuscript to address them.

read point-by-point responses

  1. Referee: [Abstract and §5] Abstract and §5 (Experiments): the central claim of outperformance 'in the majority of experimental scenarios' lacks reported error bars, statistical significance tests, or ablations isolating the dynamic-resolution component from other implementation choices; without these the evidence remains moderate and the claim is not yet load-bearing.

    Authors: We agree that additional statistical validation would make the outperformance claims more robust. In the revised manuscript we will report error bars (standard deviation over 3–5 random seeds) for all PSNR/SSIM/LPIPS numbers, include paired t-tests or Wilcoxon tests to assess statistical significance of the reported gains, and add an ablation that fixes the resolution schedule while varying only the dynamic-resolution fine-tuning and projection steps. These changes will isolate the contribution of the dynamic-resolution component. revision: yes

  2. Referee: [§3] §3 (Method, dynamic-resolution fine-tuning and projection): no quantitative analysis, bounds, or ablation is given on information retention or high-frequency loss in the subspace projection after fine-tuning; this assumption is load-bearing for the fidelity claim yet untested.

    Authors: We acknowledge that a direct quantitative assessment of information retention is currently missing. In the revised §3 we will add (i) a high-frequency retention metric obtained by wavelet decomposition (comparing energy in detail coefficients before and after projection), (ii) an ablation varying the subspace dimension while measuring both restoration quality and high-frequency loss, and (iii) a brief discussion of the observed loss and how the fine-tuning objective mitigates it for restoration tasks. These additions will directly test the load-bearing assumption. revision: yes

Circularity Check

0 steps flagged

No significant circularity; work is empirical adaptation of existing methods without self-referential derivations.

full rationale

The paper proposes projecting into lower-dimensional subspaces via dynamic resolution DMs, fine-tunes pre-trained models, and adapts DPS/DAPS into SubDPS/SubDAPS/SubDAPS++. All central claims rest on empirical evaluations across datasets rather than any derivation chain. No equations, parameters, or uniqueness results are shown to reduce by construction to fitted inputs, self-definitions, or self-citation load-bearing premises. The approach is self-contained against external benchmarks and does not invoke ansatzes or renamings that loop back to the paper's own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard diffusion model assumptions plus the domain assumption that dynamic resolution fine-tuning works without fidelity loss; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)
  • domain assumption Pre-trained diffusion models can be fine-tuned for dynamic resolution priors while retaining generative capability for restoration tasks.
    Invoked when describing the fine-tuning step to create the dynamic resolution priors.

pith-pipeline@v0.9.0 · 5514 in / 1189 out tokens · 46385 ms · 2026-05-15T02:50:30.810193+00:00 · methodology

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Reference graph

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