Abstract
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 this https URL.
Abstract (translated)
基于扩散模型的图像超分辨率（SR）旨在从相应的低分辨率（LR）观测图像中重建高分辨率（HR）图像，其面临推理效率与重建质量之间的根本性权衡。最先进的残差移位扩散框架实现了高效的4步推理，但在紧凑采样轨迹中性能严重下降。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内在次优性导致误差累积和LR先验指导不足，以及朴素双三次上采样引起的初始化偏差。本文提出LPNSR，一种先验增强的高效扩散框架以解决这些问题。我们首先为残差移位扩散范式数学推导出最优中间噪声的闭式解析解，并据此设计LR引导的多输入感知噪声预测器以替代随机高斯噪声，将LR结构先验嵌入逆向过程，同时充分保留框架核心的高效残差移位机制。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。在紧凑的4步轨迹下，LPNSR可实现端到端优化。大量实验表明，LPNSR在合成和真实世界数据集上均达到最先进的感知性能，且不依赖任何大规模文本到图像先验。我们方法的源代码可在此https URL找到。
URL
https://arxiv.org/abs/2603.21045
