OP4KSR enables efficient one-step 4K super-resolution without patches by adapting Flux with RoPE rescaling and periodicity loss to suppress artifacts.
In: European conference on computer vision
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
DS-DiT decouples low-resolution and reference interactions in a siamese diffusion transformer and adds a patch-level weights module plus autoguidance to improve reference-based super-resolution for remote sensing images.
TOC-SR builds a compact one-step diffusion model for image super-resolution achieving 6.6x fewer parameters and 2.8x fewer GMACs while maintaining strong reconstruction quality.
Allo{SR}^2 rectifies one-step super-resolution trajectories with allomorphic generative flows via SNR initialization, velocity supervision, and self-adversarial matching to deliver state-of-the-art fidelity and realism.
citing papers explorer
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OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression
OP4KSR enables efficient one-step 4K super-resolution without patches by adapting Flux with RoPE rescaling and periodicity loss to suppress artifacts.
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Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution
DS-DiT decouples low-resolution and reference interactions in a siamese diffusion transformer and adds a patch-level weights module plus autoguidance to improve reference-based super-resolution for remote sensing images.
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TOC-SR: Task-Optimal Compact diffusion for Image Super Resolution
TOC-SR builds a compact one-step diffusion model for image super-resolution achieving 6.6x fewer parameters and 2.8x fewer GMACs while maintaining strong reconstruction quality.
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Allo{SR}$^2$: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows
Allo{SR}^2 rectifies one-step super-resolution trajectories with allomorphic generative flows via SNR initialization, velocity supervision, and self-adversarial matching to deliver state-of-the-art fidelity and realism.