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GraftSR framework enhances image super-resolution with authentic texture grafting

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced GraftSR, a novel framework designed to improve real-world image super-resolution by addressing the issue of texture hallucination. GraftSR utilizes reference images of the same instance to guide the restoration of authentic textures, overcoming challenges posed by spatial misalignment between input and reference images through a dual-mask guidance mechanism. To support this approach, the team also created TexRefSR-141K, the first large-scale dataset for texture-reference-guided super-resolution, and established a new benchmark, TexRefSR-Eval, where GraftSR demonstrated state-of-the-art performance, reducing LPIPS by over 20% compared to existing methods. AI

IMPACT This research could lead to more realistic and detailed image restoration in various applications, from media enhancement to scientific imaging.

RANK_REASON The item is a research paper detailing a new method and dataset for image super-resolution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GraftSR framework enhances image super-resolution with authentic texture grafting

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The item is a research paper detailing a new method and dataset for image super-resolution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Qifan Yu, Haoran Bai, Zongyao He, Weijie He, Sibin Deng, Honggang Qi, Ying Chen ·

    GraftSR: Grafting Authentic Textures for Real-World Image Super-Resolution via Identical-Instance Guidance

    arXiv:2608.25334v1 Announce Type: new Abstract: Diffusion-based real-world image super-resolution (SR) achieves impressive perceptual quality but inherently suffers from severe texture hallucination. To overcome this limitation, we propose GraftSR, a texture-reference-guided gene…

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