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arxiv: 2605.21440 · v1 · pith:RYCXWEKQnew · submitted 2026-05-20 · 💻 cs.CV

ReMATF: Recurrent Motion-Adaptive Multi-scale Turbulence Mitigation for Dynamic Scenes

Zhiming Liu , Zhicheng Zou , Nantheera Anantrasirichai This is my paper

Pith reviewed 2026-05-21 05:04 UTC · model grok-4.3

classification 💻 cs.CV
keywords atmospheric turbulencevideo restorationrecurrent networkmotion adaptive fusionmulti-scale encoder-decodertemporal coherenceturbulence mitigationdynamic scenes
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The pith

ReMATF restores turbulence-degraded videos using only two frames at a time while preserving spatial detail and temporal stability.

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

The paper introduces a lightweight recurrent framework to mitigate atmospheric turbulence effects like warping, blur, and flickering in videos. It processes frames sequentially with a multi-scale encoder-decoder, temporal warping, and per-pixel motion-adaptive fusion to blend the warped prior output with the current prediction. This design avoids the heavy compute of multi-frame transformers while improving PSNR, SSIM, and LPIPS metrics on synthetic and real datasets. The approach targets resource-constrained scenarios where real-time restoration is needed but full temporal context is unavailable. If successful, it enables efficient deployment for applications like surveillance without sacrificing coherence.

Core claim

ReMATF restores videos through a recurrent architecture that takes only the previous output and current frame as input. A multi-scale encoder-decoder extracts features, temporal warping aligns the prior result to the current frame, and a motion-adaptive temporal fusion module performs per-pixel combination of the warped previous output and current prediction to reduce flicker and sharpen details. Experiments demonstrate consistent gains in objective and perceptual quality metrics alongside substantially faster inference than transformer baselines that require larger temporal windows.

What carries the argument

Motion-adaptive temporal fusion module that performs per-pixel fusion between the warped previous output and the current prediction to enhance coherence

If this is right

  • Supports real-time processing in resource-constrained environments due to reduced memory and compute demands compared to multi-frame methods.
  • Maintains temporal stability across dynamic scenes by recurrently carrying information from one pair of frames to the next.
  • Delivers measurable improvements in PSNR, SSIM, and perceptual quality on both synthetic and real turbulence datasets.
  • Enables deployment where access to extended frame histories is limited or latency must remain low.

Where Pith is reading between the lines

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

  • The two-frame recurrent pattern may extend to other video degradation tasks such as denoising where full temporal windows are costly.
  • Per-pixel adaptive weighting could be tested in live streaming pipelines to check if flicker reduction holds under varying motion speeds.
  • Efficiency gains might allow integration into portable imaging systems for field use without specialized hardware.

Load-bearing premise

That per-pixel motion-adaptive fusion between the warped previous output and current prediction can sufficiently enhance temporal coherence and reduce flicker without needing a larger temporal window or additional frames.

What would settle it

Observation of increased temporal flickering or lower LPIPS scores on long dynamic video sequences when the two-frame recurrent method is compared directly against a multi-frame transformer baseline under identical turbulence conditions.

Figures

Figures reproduced from arXiv: 2605.21440 by Nantheera Anantrasirichai, Zhicheng Zou, Zhiming Liu.

Figure 1
Figure 1. Figure 1: Restored results of real AT distortions from MAMAT [15] and MambaAT [36], trained on different synthetic datasets. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: y–t plane visualisation comparing recurrent temporal fusion with different weights M ∈ {0.1, 0.25, 0.5} under severe turbulence. able long-range temporal aggregation without increasing memory usage, we adopt a recurrent formulation that prop￾agates information through the previously restored frame. The current intermediate restoration Oˆ t is fused with the previous restored frame Ot−1 with exponentially d… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our proposed turbulence restoration framework. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons on synthetic ATSyn-dynamic dataset. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparisons on real-world AT from the CLEAR dataset. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison on real-world ATD [12] scenes with increasing turbulence severity (top to bottom). [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

Atmospheric turbulence severely degrades video quality by introducing distortions such as geometric warping, blur, and temporal flickering, posing significant challenges to both visual clarity and temporal consistency. Current state-of-the-art methods are based on transformer, 3D architectures and require multi-frame input, but their large computational cost and memory usage limit real-time deployment, especially in resource-constrained scenarios. In this work, we propose ReMATF, a lightweight recurrent framework that restores videos using only two frames at a time while preserving spatial detail and temporal stability. ReMATF combines a multi-scale encoder-decoder with temporal warping and a motion-adaptive temporal fusion module that performs per-pixel fusion between the warped previous output and the current prediction to enhance coherence without enlarging the temporal window. This design reduces flicker, sharpens details, and remains efficient. Experiments on synthetic and real turbulence datasets show consistent improvements in PSNR/SSIM and perceptual quality (LPIPS), along with substantially faster inference than multi-frame transformer baselines, making ReMATF suitable turbulence mitigation in resource-constrained scenarios.

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 paper proposes ReMATF, a lightweight recurrent framework for atmospheric turbulence mitigation in dynamic video scenes. It restores videos using only two frames at a time via a multi-scale encoder-decoder architecture, temporal warping, and a motion-adaptive temporal fusion module that performs per-pixel blending of the warped previous output with the current prediction. Experiments on synthetic and real turbulence datasets are reported to show consistent gains in PSNR, SSIM, and LPIPS alongside substantially faster inference than multi-frame transformer baselines.

Significance. If validated, the recurrent two-frame design with motion-adaptive fusion would represent a practical efficiency advance for real-time turbulence mitigation on resource-constrained hardware, where current multi-frame transformer methods are limited by compute and memory demands. The approach directly targets the trade-off between temporal stability and speed in video restoration.

major comments (2)
  1. [§3] §3 (Method), motion-adaptive temporal fusion description: the central claim that per-pixel blending of the warped prior output with the current prediction is sufficient to enforce long-term temporal coherence without drift or a larger temporal window is load-bearing for the efficiency argument, yet the text provides no explicit motion estimation source, residual misalignment handling, or drift-correction mechanism despite turbulence distorting motion fields.
  2. [§4] §4 (Experiments): reported PSNR/SSIM/LPIPS gains and inference speedups are shown on standard short-clip evaluations, but no long-horizon consistency metrics (e.g., temporal flicker over sequences longer than typical test clips) or ablation on fusion error propagation are included, leaving the no-drift assumption without direct support.
minor comments (2)
  1. [§3] Add an equation formalizing the per-pixel fusion operation (e.g., weighting function) in the method section for reproducibility.
  2. [§4] Clarify dataset details and full experimental protocols (train/test splits, turbulence parameters) to strengthen the empirical claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our paper. We have carefully considered the points raised regarding the method description and experimental validation, and we provide detailed responses below. We have revised the manuscript to address these concerns.

read point-by-point responses

  1. Referee: [§3] §3 (Method), motion-adaptive temporal fusion description: the central claim that per-pixel blending of the warped prior output with the current prediction is sufficient to enforce long-term temporal coherence without drift or a larger temporal window is load-bearing for the efficiency argument, yet the text provides no explicit motion estimation source, residual misalignment handling, or drift-correction mechanism despite turbulence distorting motion fields.

    Authors: We appreciate this detailed feedback on the method section. In the revised manuscript, we have expanded the description of the motion-adaptive temporal fusion module in §3. The motion estimation is performed by a dedicated lightweight optical flow estimation branch within the multi-scale encoder-decoder, which computes per-scale flow fields used for warping the previous output. Residual misalignments due to turbulence are handled by the fusion module, which generates adaptive blending weights based on both spatial features and the estimated motion confidence. This allows the network to reduce the influence of misaligned pixels. Regarding long-term coherence without drift, the per-pixel blending prioritizes the current prediction in regions of high turbulence, effectively mitigating error accumulation. We have included a diagram and additional equations to illustrate this process. We agree that an explicit drift-correction mechanism like keyframe resetting could be beneficial for extremely long sequences and have noted this as future work. revision: yes

  2. Referee: [§4] §4 (Experiments): reported PSNR/SSIM/LPIPS gains and inference speedups are shown on standard short-clip evaluations, but no long-horizon consistency metrics (e.g., temporal flicker over sequences longer than typical test clips) or ablation on fusion error propagation are included, leaving the no-drift assumption without direct support.

    Authors: We agree that demonstrating long-term temporal consistency is important for validating the recurrent design. In the revised paper, we have extended the experimental section to include evaluations on longer video sequences (up to 200 frames) from both synthetic and real datasets. We introduce a temporal flicker metric, defined as the standard deviation of temporal gradients in the restored video, to quantify consistency over extended horizons. Furthermore, we add an ablation study that simulates error propagation by varying the turbulence strength and measuring the degradation in output quality over time with and without the motion-adaptive fusion. The results show that our fusion module significantly reduces drift compared to naive recurrent baselines. These new results are presented in §4 and the supplementary material, providing direct support for the no-drift claim within practical sequence lengths. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in ReMATF architecture or claims

full rationale

The paper presents ReMATF as an independent architectural proposal: a recurrent two-frame pipeline combining a multi-scale encoder-decoder, temporal warping, and a per-pixel motion-adaptive fusion module. These elements are described as design choices motivated by efficiency and stability needs, then validated through experiments on external synthetic and real turbulence datasets. No equations, predictions, or central claims reduce by construction to fitted parameters, self-definitions, or load-bearing self-citations. The method does not rename known results or smuggle ansatzes via prior self-work; empirical metrics (PSNR/SSIM/LPIPS) and runtime comparisons serve as external evidence rather than tautological outputs. This matches the default expectation of a self-contained engineering contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract only, no explicit free parameters, axioms, or invented physical entities are detailed; the contribution centers on a new neural architecture combination whose internal hyperparameters and design choices are not enumerated here.

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