Abstract
Super-resolution (SR) applied to real-world low-resolution (LR) images often results in complex, irregular degradations that stem from the inherent complexity of natural scene acquisition. In contrast to SR artifacts arising from synthetic LR images created under well-defined scenarios, those distortions are highly unpredictable and vary significantly across different real-life contexts. Consequently, assessing the quality of SR images (SR-IQA) obtained from realistic LR, remains a challenging and underexplored problem. In this work, we introduce a no-reference SR-IQA approach tailored for such highly ill-posed realistic settings. The proposed method enables domain-adaptive IQA for real-world SR applications, particularly in data-scarce domains. We hypothesize that degradations in super-resolved images are strongly dependent on the underlying SR algorithms, rather than being solely determined by image content. To this end, we introduce a self-supervised learning (SSL) strategy that first pretrains multiple SR model oriented representations in a pretext stage. Our contrastive learning framework forms positive pairs from images produced by the same SR model and negative pairs from those generated by different methods, independent of image content. The proposed approach S3 RIQA, further incorporates targeted preprocessing to extract complementary quality information and an auxiliary task to better handle the various degradation profiles associated with different SR scaling factors. To this end, we constructed a new dataset, SRMORSS, to support unsupervised pretext training; it includes a wide range of SR algorithms applied to numerous real LR images, which addresses a gap in existing datasets. Experiments on real SR-IQA benchmarks demonstrate that S3 RIQA consistently outperforms most state-of-the-art relevant metrics.
Abstract (translated)
超分辨率（SR）技术在处理现实世界的低分辨率（LR）图像时，往往会遇到复杂的、不规则的退化现象，这些退化是由自然场景获取过程中的固有复杂性所引起的。与根据定义明确的情境生成的合成LR图像所产生的SR伪影相比，在真实生活情境中产生的这些扭曲是高度不可预测且变化多端的。因此，评估从现实世界低分辨率图像获得的超分辨图像（SR-IQA）的质量仍然是一项具有挑战性和未充分探索的问题。 在这项工作中，我们提出了一种专门针对这种高难度、非结构化现实场景的无参考SR-IQA方法。该方法能够为真实世界的SR应用提供领域自适应型IQA，在数据稀缺的情况下尤其有效。我们认为，超分辨率图像中的退化很大程度上取决于所使用的SR算法，而不仅仅是由图像内容决定的。 为此，我们引入了一种自我监督学习（SSL）策略：在预处理阶段，先对多个针对SR模型的不同表示进行预训练。我们的对比学习框架通过将同一种SR方法生成的图片配成正样本对，并将不同方法产生的图片配成负样本对来构建特征集，完全忽略图像内容的影响。 提出的S3 RIQA方法进一步包含了针对性的预处理步骤以提取互补的质量信息，并附加了一个辅助任务以便更好地应对与不同SR缩放因子相关的各种退化模式。为此，我们创建了新的数据集SRMORSS，用于支持无监督预训练；该数据集中包含广泛应用于大量真实LR图像的不同SR算法的应用实例，从而填补现有数据集的空白。 在实际SR-IQA基准测试中的实验表明，S3 RIQA方法在大多数当前最先进的相关度量标准中表现一致优异。
URL
https://arxiv.org/abs/2602.10744
