  Features obtained from object recognition CNNs have been widely used for
measuring perceptual similarities between images. Such differentiable metrics
can be used as perceptual learning losses to train image enhancement models.
However, the choice of the distance function between input and target features
may have a consequential impact on the performance of the trained model. While
using the norm of the difference between extracted features leads to limited
hallucination of details, measuring the distance between distributions of
features may generate more textures; yet also more unrealistic details and
artifacts. In this paper, we demonstrate that aggregating 1D-Wasserstein
distances between CNN activations is more reliable than the existing
approaches, and it can significantly improve the perceptual performance of
enhancement models. More explicitly, we show that in imaging applications such
as denoising, super-resolution, demosaicing, deblurring and JPEG artifact
removal, the proposed learning loss outperforms the current state-of-the-art on
reference-based perceptual losses. This means that the proposed learning loss
can be plugged into different imaging frameworks and produce perceptually
realistic results.
