Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because standard discriminators evaluate overall image naturalness rather than strict conditional realism. To address this, we propose MaCo-GAN, a novel manifold-contrastive GAN framework that replaces the conventional adversarial loss with a supervised contrastive objective. A core component of our method is a dynamic fake sample synthesizer that transforms ground truth (GT) data into a spectrum of challenging, perceptually plausible fake images that strictly maintain low-resolution (LR) correspondence. Utilizing these synthesized samples, we establish a robust contrastive minimax game: the generator is trained to attract its predictions toward on-manifold fakes (low distortion) and repel them from off-manifold fakes (high distortion), while the discriminator optimizes the exact opposite. By simply replacing the adversarial loss of a baseline SR model with our proposed objective, we demonstrate consistent improvements in the perception-distortion trade-off across various benchmarks. Extensive ablation studies validate the effectiveness of our framework and provide deep insights into the dynamics of this conditional contrastive game.

翻译：传统面向单图像超分辨率的生成对抗网络常产生伪影，根源在于标准判别器评估的是图像整体自然度而非严格的条件真实性。为此，我们提出MaCo-GAN——一种新型流形对比生成对抗框架，用监督对比目标替代传统对抗损失。该方法的核心组件是动态伪样本合成器，可将真实数据转化为一系列具有挑战性且视觉合理的伪图像，并严格保持与低分辨率图像的对应关系。利用这些合成样本，我们构建了鲁棒的对比极小极大博弈：生成器被训练为将预测结果拉向流形上的伪样本（低失真）并推开流形外的伪样本（高失真），而判别器则执行完全相反的优化。仅需将基线超分模型的对抗损失替换为所提目标函数，我们便在多个基准上持续改善了感知-失真权衡。大量消融实验验证了框架的有效性，并为这种条件对比博弈的动态机制提供了深刻见解。