  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.
