Abstract
Recent advances in generative super-resolution (SR) have greatly improved visual realism, yet existing evaluation and optimization frameworks remain misaligned with human perception. Full-Reference and No-Reference metrics often fail to reflect perceptual preference, either penalizing semantically plausible details due to pixel misalignment or favoring visually sharp but inconsistent artifacts. Moreover, most SR methods rely on ground-truth (GT)-dependent distribution matching, which does not necessarily correspond to human judgments. In this work, we propose RefReward-SR, a low-resolution (LR) reference-aware reward model for preference-aligned SR. Instead of relying on GT supervision or NR evaluation, RefReward-SR assesses high-resolution (HR) reconstructions conditioned on their LR inputs, treating the LR image as a semantic anchor. Leveraging the visual-linguistic priors of a Multimodal Large Language Models (MLLM), it evaluates semantic consistency and plausibility in a reasoning-aware manner. To support this paradigm, we construct RefSR-18K, the first large-scale LR-conditioned preference dataset for SR, providing pairwise rankings based on LR-HR consistency and HR naturalness. We fine-tune the MLLM with Group Relative Policy Optimization (GRPO) using LR-conditioned ranking rewards, and further integrate GRPO into SR model training with RefReward-SR as the core reward signal for preference-aligned generation. Extensive experiments show that our framework achieves substantially better alignment with human judgments, producing reconstructions that preserve semantic consistency while enhancing perceptual plausibility and visual naturalness. Code, models, and datasets will be released upon paper acceptance.
Abstract (translated)
生成式超分辨率（SR）的最新进展显著提升了视觉真实感，但现有的评估与优化框架仍与人类感知存在偏差。全参考与无参考指标常因像素错位而惩罚语义合理的细节，或偏好视觉锐利但不一致的伪影，难以反映感知偏好。此外，多数SR方法依赖真值（GT）监督的分布匹配，这与人类判断并不必然一致。本研究提出RefReward-SR——一种基于低分辨率（LR）参考的感知对齐奖励模型。该方法不依赖GT监督或无参考评估，而是以LR图像作为语义锚点，评估其条件生成的高分辨率（HR）重建结果。通过利用多模态大语言模型（MLLM）的视觉-语言先验，RefReward-SR以具备推理能力的方式评估语义一致性与合理性。为支持该范式，我们构建了首个大规模LR条件偏好数据集RefSR-18K，提供基于LR-HR一致性与HR自然度的成对排序。我们采用组相对策略优化（GRPO）对MLLM进行微调，使用LR条件排序奖励，并将GRPO进一步集成到SR模型训练中，以RefReward-SR作为偏好对齐生成的核心奖励信号。大量实验表明，本框架与人类判断的契合度显著提升，所生成的重建结果在增强感知合理性与视觉自然度的同时，保持了语义一致性。论文接受后，代码、模型与数据集将公开释放。
URL
https://arxiv.org/abs/2603.24198
