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arxiv: 2606.24072 · v1 · pith:QH6IH3SEnew · submitted 2026-06-23 · 💻 cs.CV

Fabric Image Demoir\'eing Benchmark from Synthesis to Restoration

Pengchao Wei , Xiaojie Guo This is my paper

Pith reviewed 2026-06-26 01:25 UTC · model grok-4.3

classification 💻 cs.CV
keywords fabric demoiréingimage restorationsynthetic datasetmoiré artifactaliasing removalbenchmark datasettextile imaging
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The pith

A synthesis framework creates the first large paired dataset for training and testing fabric demoiréing models.

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

The paper sets out to solve the lack of benchmarks for removing moiré artifacts from fabric photographs, which arise from the interaction of textile weaves with camera sensors and differ from the more regular patterns in screen moiré. It builds a dataset of 16,050 paired multi-resolution images whose aliasing can be controlled in severity, generated through a physically motivated process that aims to replicate real capture conditions. A customized baseline model is then trained on this data and reported to show promising results with good generalization to the fabric domain.

Core claim

We present the first comprehensive benchmark for fabric image demoiréing. To address the difficulty of acquiring pixel-aligned real-world pairs, we develop a physically motivated synthesis framework and construct a large-scale dataset comprising 16,050 paired multi-resolution fabric images with controllable aliasing severity. Furthermore, we customize a baseline model, which establishes promising performance on the proposed benchmark dataset with strong generalization ability.

What carries the argument

The physically motivated synthesis framework that generates paired clean and aliased fabric images by simulating the broadband semi-periodic interaction between textile patterns and sensor grids.

If this is right

  • Models trained on the new pairs can address the spectral overlap between fabric texture and aliasing that defeats screen-moiré methods.
  • The controllable severity parameter enables systematic study of how aliasing strength affects restoration difficulty.
  • The benchmark supplies a common test set for comparing future fabric-specific restoration algorithms.
  • Multi-resolution pairs support evaluation of methods across different capture scales.

Where Pith is reading between the lines

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

  • The same synthesis approach could be tested on other semi-periodic textures such as woven materials in non-fabric domains.
  • If the synthetic statistics prove transferable, the dataset size could be scaled further by varying weave parameters without new real captures.
  • The baseline customization step suggests that modest architectural changes may suffice once domain-matched training data exists.

Load-bearing premise

The synthetic images reproduce aliasing statistics close enough to real fabric photographs that models trained on them will generalize to actual captures.

What would settle it

Apply models trained solely on the synthetic pairs to a set of real captured fabric photographs and measure whether demoiréing quality matches or exceeds that of screen-moiré-trained models.

Figures

Figures reproduced from arXiv: 2606.24072 by Pengchao Wei, Xiaojie Guo.

Figure 1
Figure 1. Figure 1: Cause-to-appearance domain gap between screen moiré and fab [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Our synthesis pipeline demonstrating a fundamental domain gap. Moreover, supervised demoiréing is often sensitive to pixel-level alignment. Misalignment between moiré input im￾ages and their corresponding clean targets may cause unstable optimization and significantly degrade restoration quality. Even in the “screen is flat” ideal con￾dition, constructing pixel-aligned data requires complex registration en… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the PRISM dataset. (a) Representative synthesized moiré images in PRISM. (b) Distributions of image quality and resolution. of the fabric, making them more difficult to remove. Taking ESDNet [39] as an example, when the model is trained on the screen-camera dataset UHDM [39], it fails to effectively remove fabric moiré patterns and often suffers from unintended eradication of fabric textures. T… view at source ↗
Figure 4
Figure 4. Figure 4: Pipeline of our method. 4 FaDeNet: Proposed Fabric Demoiréing Network We propose FaDeNet for fabric demoiréing. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visual comparison of error maps on the PRISM dataset. The color transition from dark purple to bright yellow represents increasing absolute errors (|Output−GT|). Qualitative results [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results on real-world cases by models trained only on PRISM Blind MOS user study. To further assess subjective visual appeal, we con￾ducted a blind Mean Opinion Score (MOS) study. We selected five representative models and evaluated them across 15 groups of representative real-world images. The study involved 20 participants, including 10 domain experts in image pro￾cessing and 10 volunteers. A… view at source ↗
Figure 1
Figure 1. Figure 1: Representative source patches and corresponding log-magnitude 2D Fourier spectrum for screen and fabric domains. Under the same crop size and FFT normal￾ization, the screen source shows a regular lattice structure and a relatively sparse, grid-like spectrum, whereas the fabric source exhibits broader and more distributed spectral support with stronger directional spread and local variation. This indicates … view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of sampling outcomes in screen and fabric moiré. Left: screen moiré remains largely unchanged when the displayed content is replaced, indicating that the artifact is mainly governed by the mismatch between the display lattice and the camera sampling lattice, rather than by semantic image content. Right: for fabric moiré, we visualize the clean image, the moiré image, the estimated residual, and … view at source ↗
Figure 3
Figure 3. Figure 3: PRISM paired data examples. Each clean GT image is paired with multiple synthesized moiré observations under different sampled imaging parameters. B.2 Pair Synthesis Based on these GT images, we further constructed paired data using the pro￾posed PRISM pipeline. For each GT image, we adopted a one-to-many synthesis strategy and generated up to 15 moiré variants under different randomly sam￾pled physical pa… view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of learned spatial confidence masks on representative PRISM training samples. For each example, the left image is the moiré input and the right image is the corresponding predicted mask. Warmer colors indicate higher confidence and therefore stronger correction strength. The learned masks mainly focus on textile￾rich regions with dense repetitive structures and strong moiré contamination, whi… view at source ↗
Figure 5
Figure 5. Figure 5: Failure cases. Second, the mask is also sensitive to the spatial distribution and severity of the artifacts. For relatively mild cases, the response is more localized and sparse, whereas for images with globally strong interference the mask becomes broader and more active over most of the garment area. This behavior is consistent with the design goal of FaDeNet: instead of applying uniform restoration ever… view at source ↗
Figure 6
Figure 6. Figure 6: Visual comparison of error maps on the PRISM dataset (Part I) [PITH_FULL_IMAGE:figures/full_fig_p027_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visual comparison of error maps on the PRISM dataset (Part II) [PITH_FULL_IMAGE:figures/full_fig_p028_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visual comparison of error maps on the PRISM dataset (Part III) [PITH_FULL_IMAGE:figures/full_fig_p029_8.png] view at source ↗
read the original abstract

Fabric moir\'e is a sampling-induced aliasing artifact caused by the interaction between fine textile patterns and camera sensor grids, producing structured interference that severely degrades image quality. Unlike screen-induced moir\'e, which stems from strictly periodic display lattices, fabric moir\'e is intrinsically more challenging due to the broadband and semi-periodic nature of textile weaves. The heavy spectral overlap between intrinsic texture and aliasing components renders fabric demoir\'eing substantially more ill-posed. Consequently, existing models trained on screen moir\'e datasets generalize poorly to these complex textile patterns. Despite its practical importance, fabric image demoir\'eing remains underexplored and lacks standardized benchmarks. We present the first comprehensive benchmark for fabric image demoir\'eing. To address the difficulty of acquiring pixel-aligned real-world pairs, we develop a physically motivated synthesis framework and construct a large-scale dataset comprising 16,050 paired multi-resolution fabric images with controllable aliasing severity. Furthermore, we customize a baseline model, which establishes promising performance on the proposed benchmark dataset with strong generalization ability. Our benchmark provides a standardized platform for advancing research in fabric image demoir\'eing.

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 / 1 minor

Summary. The paper claims to introduce the first comprehensive benchmark for fabric image demoiréing, an underexplored problem distinct from screen moiré due to broadband semi-periodic textile patterns. To overcome the lack of pixel-aligned real pairs, it develops a physically motivated synthesis framework that generates a dataset of 16,050 paired multi-resolution fabric images with controllable aliasing severity. A customized baseline model is presented that reportedly achieves promising performance and strong generalization on this benchmark.

Significance. If the synthetic pairs faithfully capture real fabric moiré statistics, the work would supply a standardized, large-scale resource for an ill-posed restoration task where existing screen-moiré models fail, potentially accelerating research on spectral-overlap artifacts.

major comments (2)
  1. [Abstract] Abstract: the claims of 'promising performance' and 'strong generalization ability' are stated without any quantitative metrics, PSNR/SSIM values, error analysis, or comparisons to prior demoiréing methods, leaving the central empirical contribution unsupported in the available text.
  2. [Abstract] Abstract (synthesis framework paragraph): the assertion that the physically motivated synthesis produces aliasing statistics matching real fabric captures is load-bearing for all generalization claims, yet no spectrum-matching statistics, real-image ablation, or distribution-distance measures are provided to anchor the synthetic distribution inside the real one.
minor comments (1)
  1. [Abstract] The abstract refers to 'multi-resolution' images but does not specify the exact resolution pyramid or downsampling factors used in dataset construction.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that the claims require quantitative support and validation of the synthesis framework. We will revise the manuscript to address both points.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claims of 'promising performance' and 'strong generalization ability' are stated without any quantitative metrics, PSNR/SSIM values, error analysis, or comparisons to prior demoiréing methods, leaving the central empirical contribution unsupported in the available text.

    Authors: We agree that the abstract should include quantitative evidence. In the revised version, we will add specific PSNR/SSIM values from the baseline model experiments, along with comparisons to prior demoiréing methods, to directly support the performance and generalization claims. revision: yes

  2. Referee: [Abstract] Abstract (synthesis framework paragraph): the assertion that the physically motivated synthesis produces aliasing statistics matching real fabric captures is load-bearing for all generalization claims, yet no spectrum-matching statistics, real-image ablation, or distribution-distance measures are provided to anchor the synthetic distribution inside the real one.

    Authors: The synthesis is derived from a physical model of broadband aliasing in textile imaging. To strengthen the validation, we will add spectrum-matching comparisons, real-image ablations, and distribution-distance measures (e.g., spectrum analysis and statistical distances) between synthetic and captured real moiré patterns in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset and benchmark construction without self-referential derivations

full rationale

The paper presents a synthesis framework to generate a paired dataset of 16,050 fabric images and a customized baseline model for demoiréing. No equations, predictions, or uniqueness claims appear that reduce by construction to fitted inputs, self-citations, or ansatzes from prior author work. The synthesis is motivated by the acknowledged difficulty of obtaining real pixel-aligned pairs, but the contribution remains an empirical benchmark rather than a closed derivation chain; the central claims rest on the new data and model performance rather than any load-bearing step that is definitionally equivalent to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review limits visibility into parameters; the core unverified element is the fidelity of the synthesis process to real data.

axioms (1)
  • domain assumption Physically motivated synthesis framework produces data whose aliasing matches real fabric photographs sufficiently for generalization
    Invoked to justify the benchmark's utility; if false, trained models will not transfer.

pith-pipeline@v0.9.1-grok · 5730 in / 1199 out tokens · 19309 ms · 2026-06-26T01:25:40.114376+00:00 · methodology

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