Abstract
This paper presents Fd-CycleGAN, an image-to-image (I2I) translation framework that enhances latent representation learning to approximate real data distributions. Building upon the foundation of CycleGAN, our approach integrates Local Neighborhood Encoding (LNE) and frequency-aware supervision to capture fine-grained local pixel semantics while preserving structural coherence from the source domain. We employ distribution-based loss metrics, including KL/JS divergence and log-based similarity measures, to explicitly quantify the alignment between real and generated image distributions in both spatial and frequency domains. To validate the efficacy of Fd-CycleGAN, we conduct experiments on diverse datasets -- Horse2Zebra, Monet2Photo, and a synthetically augmented Strike-off dataset. Compared to baseline CycleGAN and other state-of-the-art methods, our approach demonstrates superior perceptual quality, faster convergence, and improved mode diversity, particularly in low-data regimes. By effectively capturing local and global distribution characteristics, Fd-CycleGAN achieves more visually coherent and semantically consistent translations. Our results suggest that frequency-guided latent learning significantly improves generalization in image translation tasks, with promising applications in document restoration, artistic style transfer, and medical image synthesis. We also provide comparative insights with diffusion-based generative models, highlighting the advantages of our lightweight adversarial approach in terms of training efficiency and qualitative output.
Abstract (translated)
本文提出了Fd-CycleGAN，这是一种图像到图像（I2I）转换框架，它通过增强潜在表示学习来逼近真实数据分布。基于CycleGAN的基础，我们的方法整合了局部邻域编码（LNE）和频率感知监督，以捕捉源领域的精细局部像素语义并保持结构一致性。我们采用基于分布的损失度量，包括KL/JS散度和对数相似性测量，明确量化真实图像和生成图像在空间和频域分布之间的对齐程度。 为了验证Fd-CycleGAN的有效性，我们在多种数据集上进行了实验——Horse2Zebra、Monet2Photo以及合成增强的Strike-off数据集。与基线CycleGAN和其他最先进的方法相比，我们的方法展示了更优的感知质量、更快的收敛速度和改进的模态多样性，尤其是在低数据环境下。 通过有效捕捉局部和全局分布特征，Fd-CycleGAN实现了更加视觉上一致且语义一致的转换。我们的结果显示，在图像翻译任务中频率引导的潜在学习显著提高了泛化能力，并在文档恢复、艺术风格转移以及医学图像合成等领域具有应用前景。此外，我们还提供了与基于扩散的生成模型比较分析的结果，突出了我们轻量级对抗方法在训练效率和定性输出方面的优势。
URL
https://arxiv.org/abs/2508.03415
