arXiv Computer Vision
DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution
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Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
The paper introduces SMFSR, a one‑step real‑world super‑resolution framework that retains the noise‑started generation of diffusion models while conditioning on low‑resolution images. It uses LR‑conditioned SplitMeanFlow with Interval Splitting Consistency to collapse a multi‑step trajectory into a single average‑velocity prediction, and adds a GAN refinement stage with a DINOv3 discriminator and variational score distillation to enhance texture realism. Experiments show SMFSR achieves state‑of‑the‑art perceptual quality among one‑step diffusion‑based methods while maintaining fast inference.
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Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution
arXiv:2607. 15711v1 Announce Type: cross Abstract: Diffusion-based methods have achieved impressive performance in real-world image super-resolution (Real-ISR) by leveraging large pre-trained stable diffusion (SD) models as powerful generative priors.
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When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution
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arXiv Computer Vision
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PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution
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arXiv Computer Vision
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Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution
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