Fast and faithful scale-aware image filters

This paper proposes a fast and accurate computational framework for scale-aware image filters. Our framework is based on accurately approximating $$L^{1}$$ L 1 Gaussian convolution with respect to a transformed pixel domain representing geodesic distance on a guidance image manifold in order to recover salient edges in a manner faithful to scale-space theory while removing small image structures. Our framework possesses linear computational complexity with high approximation precision. We examined it numerically in terms of speed, accuracy, and quality compared with conventional methods.

Human Sensitivity to Perturbations Constrained by a Model of the Natural Image Manifold

Humans are remarkably well tuned to the statistical properties of natural images. However, quantitative characterization of processing within the domain of natural images has been difficult because most parametric manipulations of a natural image make that image appear less natural. We used generative adversarial networks (GANs) to constrain parametric manipulations to remain within an approximation of the manifold of natural images. In the first experiment, 7 observers decided which one of two synthetic perturbed images matched a synthetic unperturbed comparison image. Observers were significantly more sensitive to perturbations that were constrained to an approximate manifold of natural images than they were to perturbations applied directly in pixel space. Trial by trial errors were consistent with the idea that these perturbations disrupt configural aspects of visual structure used in image segmentation. In a second experiment, 5 observers discriminated paths along the image manifold as recovered by the GAN. Observers were remarkably good at this task, confirming that observers were tuned to fairly detailed properties of an approximate manifold of natural images. We conclude that human tuning to natural images is more general than detecting deviations from natural appearance, and that humans have, to some extent, access to detailed interrelations between natural images.