Diffusion-based image super-resolution (SR), which aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) observations, faces a fundamental trade-off between inference efficiency and reconstruction quality. The state-of-the-art residual-shifting diffusion framework achieves efficient 4-step inference, yet suffers from severe performance degradation in compact sampling trajectories. This is mainly attributed to two core limitations: the inherent suboptimality of unconstrained random Gaussian noise in intermediate steps, which leads to error accumulation and insufficient LR prior guidance, and the initialization bias caused by naive bicubic upsampling. In this paper, we propose LPNSR, a prior-enhanced efficient diffusion framework to address these issues. We first mathematically derive the closed-form analytical solution of the optimal intermediate noise for the residual-shifting diffusion paradigm, and accordingly design an LR-guided multi-input-aware noise predictor to replace random Gaussian noise, embedding LR structural priors into the reverse process while fully preserving the framework's core efficient residual-shifting mechanism. We further mitigate initial bias with a high-quality pre-upsampling network to optimize the diffusion starting point. With a compact 4-step trajectory, LPNSR can be optimized in an end-to-end manner. Extensive experiments demonstrate that LPNSR achieves state-of-the-art perceptual performance on both synthetic and real-world datasets, without relying on any large-scale text-to-image priors. The source code of our method can be found at https://github.com/Faze-Hsw/LPNSR.

翻译：基于扩散模型的图像超分辨率旨在从低分辨率观测中重建高分辨率图像，其面临推理效率与重建质量之间的根本性权衡。当前最先进的残差移位扩散框架虽能实现高效的四步推理，但在紧凑采样轨迹中仍存在严重的性能退化现象。这主要归因于两个核心局限：中间步骤中无约束随机高斯噪声的固有次优性（导致误差累积与低分辨率先验引导不足），以及朴素双三次上采样引发的初始化偏差。本文提出先验增强高效扩散框架LPNSR以解决上述问题。我们首先数学推导出残差移位扩散范式下最优中间噪声的闭式解析解，并据此设计低分辨率引导的多输入感知噪声预测器替代随机高斯噪声，在完整保留框架核心高效残差移位机制的同时，将低分辨率结构先验嵌入逆向过程。我们进一步通过高质量预上采样网络缓解初始偏差以优化扩散起点。借助紧凑的四步采样轨迹，LPNSR可实现端到端优化。大量实验表明，LPNSR在合成与真实数据集上均达到最先进的感知性能，且无需依赖任何大规模文本到图像先验。本方法源代码可在https://github.com/Faze-Hsw/LPNSR 获取。