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arxiv: 2605.13258 · v2 · pith:ZF52M5T2new · submitted 2026-05-13 · 💻 cs.CV · cs.AI

X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge

Youwei Pan , Leilei Cao , Yingfang Zhu , Fengjie Zhu This is my paper

Pith reviewed 2026-06-30 21:59 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords image restorationall-weather conditionstransformer modeledge-aware lossmodel ensembletwo-stage trainingcomputer vision challenge
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The pith

Two-stage training with a gradient-guided edge loss and weighted model ensemble secured first place in the all-weather image restoration challenge.

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

The paper presents the first-place solution for restoring images degraded by weather conditions such as rain, snow, haze, and their combinations. It extends an existing transformer baseline by adding a loss function that uses Sobel operators to weight supervision toward edges and high-frequency regions. Training occurs in two stages, first pretraining one model on a large diverse dataset covering multiple degradations and then fine-tuning a second model on weather-specific data. At inference the two outputs are combined with fixed weights that favor the broadly pretrained model. This combination achieved the top ranking in the competition.

Core claim

The central claim is that a two-stage training process on a large multi-degradation dataset followed by weather-specific fine-tuning, combined with a Gradient-Guided Edge-Aware loss and a 0.4/0.6 weighted ensemble of the resulting models, produces the highest-performing system for all-weather image restoration on the challenge test set.

What carries the argument

The Gradient-Guided Edge-Aware (GGEA) Loss, which applies Sobel operators to the ground-truth image to build a spatially adaptive weight map that assigns higher supervision to edge and high-frequency regions.

If this is right

  • The two-stage strategy enables efficient domain adaptation when only limited weather-specific data is available.
  • The edge-aware loss improves structural detail preservation when added to standard L1 and multi-scale SSIM objectives.
  • The weighted ensemble balances broad generalization from large-scale pretraining with targeted adaptation from fine-tuning.
  • The overall pipeline handles composite degradations such as simultaneous rain and haze more effectively than single-stage training.

Where Pith is reading between the lines

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

  • Similar staged pretraining and fine-tuning could be tested on other restoration tasks where domain-specific data is scarce relative to general data.
  • The GGEA loss could be applied to different backbone architectures to check whether the edge-weighting benefit holds beyond the Restormer family.
  • Validation-based adjustment of the 0.4/0.6 ensemble ratio might improve performance if the hidden test distribution differs from the training splits.

Load-bearing premise

The specific ensemble weights, two-stage data splits, and loss-component balance will yield superior results on the hidden challenge test distribution.

What would settle it

Re-running the full pipeline without the GGEA loss or with altered ensemble weights and measuring whether the method loses its top ranking on the official challenge test set.

Figures

Figures reproduced from arXiv: 2605.13258 by Fengjie Zhu, Leilei Cao, Yingfang Zhu, Youwei Pan.

Figure 1
Figure 1. Figure 1: The overall framework of our proposed method. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative results on challenging scenes (Part 1). Each scene shows the degraded input and our restored output. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative results on challenging scenes (Part 1). Each scene shows the degraded input and our restored output. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results on challenging scenes (Part 2). Each scene shows the degraded input and our restored output. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results on challenging scenes (Part 2). Each scene shows the degraded input and our restored output. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

In this work, we present our winning solution for the 8th UG2+ Challenge (CVPR 2026) Track 1: Image Restoration under All-weather Conditions. Our method is built upon the X-Restormer baseline, which captures both channel-wise global dependencies and spatially-local structural information through its dual-attention design (Multi-DConv Head Transposed Attention and Overlapping Cross-Attention), augmented with the spatially-adaptive input scaling mechanism from Restormer-Plus. We adopt a two-stage training strategy with dual-model ensemble inference. In the first stage, Model B is trained from scratch on a large-scale diverse dataset randomly sampled from the FoundIR training set (approximately 800 GB out of 4.84 TB), covering five degradation types: blur, haze, rain, snow, and composite conditions such as co-occurring rain and haze. In the second stage, Model A is fine-tuned on the WeatherStream dataset (rain and snow splits) using Model B's final checkpoint as pretrained initialization, enabling efficient domain adaptation with a substantially smaller dataset. To better preserve structural details during training, we propose a novel Gradient-Guided Edge-Aware (GGEA) Loss, which applies Sobel operators to the ground-truth image to construct a spatially adaptive weight map that assigns higher supervision to edge and high-frequency regions. This is incorporated alongside L1 and Multi-Scale SSIM losses in a unified training objective. At inference time, predictions from the two models are fused via a weighted average, out = 0.4 x outA + 0.6 x outB, where the higher weight assigned to Model B reflects its stronger generalization ability from large-scale pretraining. With these strategies, our proposed method successfully ranks 1st in the challenge.

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

0 major / 4 minor

Summary. The manuscript presents the authors' 1st-place entry in the UG2+ CVPR 2026 All-Weather Restoration Challenge (Track 1). The solution augments the X-Restormer baseline (with Multi-DConv Head Transposed Attention, Overlapping Cross-Attention, and Restormer-Plus input scaling) by a novel Gradient-Guided Edge-Aware (GGEA) loss that uses Sobel-derived spatial weights, a two-stage training regimen (large-scale pretraining on a ~800 GB FoundIR subset covering five degradation types, followed by fine-tuning on WeatherStream), and a 0.4/0.6 weighted ensemble of the fine-tuned and pretrained models at inference.

Significance. The external 1st-place ranking on the hidden test set provides concrete validation that the described pipeline is effective for all-weather restoration. The GGEA loss and two-stage pretraining/fine-tuning strategy constitute reusable engineering contributions that could be adopted in other restoration pipelines; the manuscript also supplies a concrete, reproducible recipe (dataset splits, ensemble coefficients) that achieved top performance under challenge conditions.

minor comments (4)
  1. [Abstract] Abstract: the precise scalar weights multiplying the GGEA, L1, and Multi-Scale SSIM terms in the unified objective are omitted; supplying these coefficients (or stating they were tuned on a held-out validation split) would improve reproducibility without altering the central claim.
  2. [Abstract] Abstract: the exact number of images or patches drawn from the 800 GB FoundIR subset, the number of training epochs or iterations per stage, and the optimizer/learning-rate schedule are not stated; these details are standard for competition-solution papers and would allow readers to replicate the reported ranking.
  3. [Abstract] Abstract: the challenge evaluation metric(s) (PSNR, SSIM, or a composite score) on which the 1st-place ranking was determined are not named; adding this information clarifies the quantitative basis of the result.
  4. The manuscript would benefit from a short table or paragraph summarizing the contribution of each added component (GGEA loss, two-stage schedule, ensemble) even if only on the public validation set; such a table is conventional in solution papers and does not require new experiments.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive summary of our manuscript, recognition of its significance, and recommendation for minor revision. We appreciate the acknowledgment that the 1st-place result, GGEA loss, and two-stage training provide concrete, reusable contributions with a reproducible recipe.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a competition solution report whose central claim is an externally verified 1st-place ranking on a hidden test set. The described pipeline (X-Restormer baseline, GGEA loss, two-stage training, 0.4/0.6 ensemble) contains no mathematical derivation, no fitted parameter renamed as a prediction, and no load-bearing self-citation chain. All components are standard engineering choices whose performance is measured against an organizer-provided benchmark rather than against quantities defined from the method's own inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 1 invented entities

Only the abstract is available, so the ledger records only elements explicitly named there. The ensemble weights are stated numerically; the GGEA loss is introduced without external validation; the effectiveness of the two-stage regime is taken as a domain assumption.

free parameters (2)
  • ensemble weights = 0.4 and 0.6
    Explicitly stated as 0.4 for Model A and 0.6 for Model B to favor the large-scale model
  • loss-component weights
    L1, Multi-Scale SSIM, and GGEA are combined but numerical coefficients are not given
axioms (2)
  • domain assumption X-Restormer dual-attention blocks capture both channel-wise global dependencies and spatially-local structure
    Invoked when the method is described as built upon the X-Restormer baseline
  • domain assumption Large-scale pretraining followed by fine-tuning on a smaller domain-specific set yields better generalization than single-stage training
    Central to the two-stage strategy described
invented entities (1)
  • Gradient-Guided Edge-Aware (GGEA) Loss no independent evidence
    purpose: Constructs a spatially adaptive weight map from Sobel edges to emphasize high-frequency regions during supervision
    New loss function proposed in the abstract with no independent evidence supplied beyond the challenge ranking

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Reference graph

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