Update README.md

README.md

@@ -9,26 +9,22 @@ tags:
 - diffusion
 ---
 
-# LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction
+# LPNSR: Optimal Noise-Guided Diffusion Image Super-Resolution Via Learnable Noise Prediction
 
 This repository contains the official model weights for **LPNSR**, a prior-enhanced efficient diffusion framework for image super-resolution (SR).
 
-- **Paper:** [LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction](https://huggingface.co/papers/2603.21045)
+- **Paper:** [LPNSR: Optimal Noise-Guided Diffusion Image Super-Resolution Via Learnable Noise Prediction](https://huggingface.co/papers/2603.21045)
 - **GitHub Repository:** [Faze-Hsw/LPNSR](https://github.com/Faze-Hsw/LPNSR)
 
 ## Introduction
 
-LPNSR addresses the efficiency-quality trade-off in diffusion-based SR. While state-of-the-art frameworks like ResShift achieve efficient 4-step inference, they can suffer from performance degradation due to unconstrained random noise. LPNSR addresses this by:
-- Replacing random Gaussian noise with an **LR-guided multi-input-aware noise predictor**, embedding structural priors into the reverse process.
-- Mitigating initialization bias using a **high-quality pre-upsampling network** to optimize the diffusion starting point.
-- Maintaining a compact 4-step sampling trajectory for high-quality, real-world super-resolution.
+Diffusion-based image super-resolution (SR) often suffers from inconsistent performance due to random noise injection, especially when using a limited number of sampling steps. To address this, **LPNSR** introduces a theoretical framework for optimal noise injection.
 
-## Features
-
-- **Efficient Sampling**: Only 4 sampling steps required for high-quality super-resolution.
-- **Noise Predictor**: Learns to predict optimal noise maps for partial diffusion initialization.
-- **Real-world SR**: Designed to handle complex real-world degradations.
-- **SwinIR Integration**: Optional SwinIR refinement for enhanced details.
+### Key Innovations:
+* **Optimal Noise Framework:** Derives a closed-form analytical solution for optimal intermediate noise based on maximum likelihood estimation.
+* **Learnable Noise Predictor:** Replaces standard random Gaussian noise with an LR-guided multi-input-aware noise predictor.
+* **Reduced Initialization Bias:** Uses a high-quality pre-upsampling network to improve starting conditions.
+* **Efficient 4-Step Trajectory:** A compact trajectory uniquely enables end-to-end optimization of the entire reverse chain, achieving state-of-the-art perceptual quality on synthetic and real-world datasets without large-scale text-to-image priors.
 
 ## Quick Start
 
@@ -56,7 +52,7 @@ If you find this work useful, please cite:
 
 ```bibtex
 @article{lpnsr2026,
-  title={LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction},
+  title={LPNSR: Optimal Noise-Guided Diffusion Image Super-Resolution Via Learnable Noise Prediction},
   author={Huang, Shuwei and Liu, Shizhuo and Wei, Zijun},
   journal={arXiv preprint arXiv:2603.21045},
   year={2026},