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AIM 2019 Challenge on Image Demoireing: Dataset and Study

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arxiv 1911.02498 v1 pith:I2U4U4NI submitted 2019-11-06 cs.CV

Shanxin Yuan , Radu Timofte , Gregory Slabaugh , Ales Leonardis This is my paper · ORCID
classification cs.CV
keywords imagedatasetchallengedemoireingadditionadvancescalledclean
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces a novel dataset, called LCDMoire, which was created for the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. The dataset comprises 10,200 synthetically generated image pairs (consisting of an image degraded by moire and a clean ground truth image). In addition to describing the dataset and its creation, this paper also reviews the challenge tracks, competition, and results, the latter summarizing the current state-of-the-art on this dataset.

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  1. Moir\'eXNet: Adaptive Multi-Scale Demoir\'eing with Linear Attention Test-Time Training and Truncated Flow Matching Prior

    cs.CV 2025-06 reject novelty 4.0 of 10

    A RAW-to-sRGB demoireing model built from linear-attention blocks and a truncated flow-matching refinement step reports state-of-the-art PSNR and SSIM on two benchmarks, with internal reporting inconsistencies.

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