Recent advancements in ultra-high-resolution unpaired image-to-image translation have aimed to mitigate the constraints imposed by limited GPU memory through patch-wise inference. Nonetheless, existing methods often compromise between the reduction of noticeable tiling artifacts and the preservation of color and hue contrast, attributed to the reliance on global image- or patch-level statistics in the instance normalization layers. In this study, we introduce a Dense Normalization (DN) layer designed to estimate pixel-level statistical moments. This approach effectively diminishes tiling artifacts while concurrently preserving local color and hue contrasts. To address the computational demands of pixel-level estimation, we further propose an efficient interpolation algorithm. Moreover, we invent a parallelism strategy that enables the DN layer to operate in a single pass. Through extensive experiments, we demonstrate that our method surpasses all existing approaches in performance. Notably, our DN layer is hyperparameter-free and can be seamlessly integrated into most unpaired image-to-image translation frameworks without necessitating retraining. Overall, our work paves the way for future exploration in handling images of arbitrary resolutions within the realm of unpaired image-to-image translation. Code is available at: https://github.com/Kaminyou/Dense-Normalization.

翻译：近年来，超高分辨率无配对图像到图像转换领域的研究进展致力于通过分块推理缓解有限GPU内存带来的约束。然而，现有方法往往需要在减少明显分块伪影与保持色彩和色调对比度之间进行折衷，这归因于实例归一化层对全局图像或分块级统计量的依赖。在本研究中，我们提出了一种密集归一化（DN）层，旨在估计像素级统计矩。该方法能有效减少分块伪影，同时保持局部色彩与色调对比度。为应对像素级估计的计算需求，我们进一步提出了一种高效插值算法。此外，我们设计了一种并行化策略，使DN层能够以单次前向传播运行。通过大量实验，我们证明所提方法在性能上超越所有现有方法。值得注意的是，我们的DN层无需超参数调整，并且能够无缝集成到大多数无配对图像到图像转换框架中，无需重新训练。总体而言，我们的工作为未来在无配对图像到图像转换领域处理任意分辨率图像的研究开辟了道路。代码发布于：https://github.com/Kaminyou/Dense-Normalization。