arXiv Computer Vision
DISRQAD: Diffusion Image Super-Resolution Quality Assessment Dataset and Benchmark
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Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution
UGDiff introduces an uncertainty-guided diffusion paradigm for single-image super-resolution, aiming to improve the perception‑distortion trade‑off. The method estimates reconstruction uncertainty of latent features from a high‑fidelity image and uses this uncertainty, along with diffusion sampler posterior variance, to selectively restore high‑frequency details in uncertain regions while preserving fidelity elsewhere. Experiments show that UGDiff outperforms state‑of‑the‑art diffusion‑based SR methods.
arXiv Machine Learning
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arXiv Computer Vision
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DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution
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Hugging Face Trending Papers
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Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution
Real-world image super-resolution (Real-ISR) aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations. Recent advances have leveraged the LQ inputs and natural image priors learned by Stable Diffusion models to achieve impressive results.
arXiv Computer Vision
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Implicit Neural Representation-Based Continuous Single Image Super-Resolution: An Empirical Benchmark
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arXiv Computer Vision
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PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution
arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while...