LPNSR: Learnable Noise Prediction for Diffusion-Based Image Super-Resolution

arXiv:2603.21045 · cs.CV, cs.AI · Submitted 2026-03-22 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-03-22

Updated: 2026-09-14

Code: https://github.com/Faze-Hsw/LPNSR

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Diffusion Model — 19× in this paper · explained in 8 episodes
• Diffusion Models — 16× in this paper · explained in 6 episodes
• Conference on Computer Vision and Pattern Recognition — 12× in this paper
• Fidelity — 10× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Core — 7× in this paper · explained in 2 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Loss Function — 5× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Inverse Problem — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Over-smoothing — 2× in this paper · explained in 4 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Denoising Diffusion Implicit Models
• Diffusion Posterior Sampling for General Noisy Inverse Problems
• Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
• Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
• Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution
• Decoupled Weight Decay Regularization
• SGDR: Stochastic Gradient Descent with Warm Restarts

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