DPAMixerSR: An Efficient Degradation-Pattern-Aware Model for Image Super-Resolution

arXiv:2609.32705 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Fidelity — 11× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Adaptive Sparse Processing — 8× in this paper
• Aggregation — 8× in this paper · explained in 1 episode
• Perceptual Degradation Ranking — 7× in this paper
• Distillation — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Spatial Structure Adjustment — 5× in this paper
• Benchmark — 4× in this paper · explained in 2 episodes
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Sparsity — 4× in this paper · explained in 4 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• Multi-scale Feature Extraction — 2× in this paper
• Pruning — 2× in this paper · explained in 4 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Minimal Spanning Propagation Structure — 1× in this paper
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Perceptual Degradation Score — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Spatial Structure Adjustment Block — 1× in this paper
• Two-stage training — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution

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