Abstract
Diffusion-based super-resolution can synthesize rich details, but models trained on synthetic paired data often fail on real-world LR images due to distribution shifts. We propose Bird-SR, a bidirectional reward-guided diffusion framework that formulates super-resolution as trajectory-level preference optimization via reward feedback learning (ReFL), jointly leveraging synthetic LR-HR pairs and real-world LR images. For structural fidelity easily affected in ReFL, the model is directly optimized on synthetic pairs at early diffusion steps, which also facilitates structure preservation for real-world inputs under smaller distribution gap in structure levels. For perceptual enhancement, quality-guided rewards are applied at later sampling steps to both synthetic and real LR images. To mitigate reward hacking, the rewards for synthetic results are formulated in a relative advantage space bounded by their clean counterparts, while real-world optimization is regularized via a semantic alignment constraint. Furthermore, to balance structural and perceptual learning, we adopt a dynamic fidelity-perception weighting strategy that emphasizes structure preservation at early stages and progressively shifts focus toward perceptual optimization at later diffusion steps. Extensive experiments on real-world SR benchmarks demonstrate that Bird-SR consistently outperforms state-of-the-art methods in perceptual quality while preserving structural consistency, validating its effectiveness for real-world super-resolution.
Abstract (translated)
基于扩散的超分辨率可以合成丰富的细节，但利用合成配对数据训练的模型在处理真实世界的低分辨率（LR）图像时往往因分布偏移而失效。我们提出了Bird-SR，这是一种双向奖励引导的扩散框架，它通过奖赏反馈学习（ReFL），将超分辨率问题定义为基于轨迹水平偏好优化的问题，并同时利用合成的低分辨-高分辨配对数据和真实世界的低分辨图像。为了在ReFL中容易受到影响的结构保真度，在早期的扩散步骤中直接针对合成的数据进行模型优化，这也有助于在结构层次上的分布差距较小时保持真实输入的结构一致性。对于感知增强，在后期采样步骤中对合成和真实低分辨率图像都应用了质量导向的奖赏。 为了缓解奖励操控问题，我们通过以其清晰对应物为界来限定合成结果的相对优势空间来构建其奖项制度，同时在真实世界的优化过程中加入语义对齐约束以进行正则化。此外，为了平衡结构和感知学习，我们采用了一种动态保真度-感知权衡策略，在早期阶段强调结构保持，并逐步将注意力转移到后期扩散步骤中的感知优化。 通过真实的超分辨率基准实验表明，Bird-SR在感知质量上持续优于最先进的方法，同时保持了结构一致性，这验证了它对现实世界超分辨率的有效性。
URL
https://arxiv.org/abs/2602.07069
