PixelSR: Pixel-Space Orthogonal Flow Decoupling for Real-World Image Super-Resolution
To address this challenge, we propose PixelSR, a novel single-stage Pixel Diffusion Transformer for Real-SR that operates directly in pixel space.
ProofPaper ↗
Key points
- Latent diffusion models have recently achieved remarkable progress in Real-world Image Super-Resolution (Real-SR).
- However, existing methods consistently exhibit a persistent seesaw effect: enhancing perceptual realism compromises structural fidelity.
- Specifically, PixelSR consists of two core designs: Pixel-space Conditional Diffusion and Orthogonal Flow Decoupling.
- The former establishes an effective pixel-space restoration framework by integrating spatially aligned LR conditioning, visual semantic guidance, and lightweight texture refinement, thereby bypassing VAE compression and enabling end-to-end optimization directly in RGB space.
Sources (1)
- [1]PixelSR: Pixel-Space Orthogonal Flow Decoupling for Real-World Image Super-ResolutionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 07:21 AM
To address this challenge, we propose PixelSR, a novel single-stage Pixel Diffusion Transformer for Real-SR that operates directly in pixel space.
Latent diffusion models have recently achieved remarkable progress in Real-world Image Super-Resolution (Real-SR).
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026Push-Pull Attentional Anchoring for Diffusion Concept Erasure
- Oct 19, 2026Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcasting
- Oct 19, 2026SWIFT: Self-guided Weak-to-strong dIFfusion models via Target extrapolation
- Oct 19, 2026RoAR-VAE: Improving Latent Diffusability through Robust Prediction
- Oct 18, 2026PanoDent-KD: From Generalist Knowledge Distillation to Cross-Dataset Specialist Detection and Embedded Deployment in Panoramic Radiographs
- Oct 18, 2026PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation