Towards Metamerism via Foveated Style Transfer.

Given the recent successes of deep learning applied to style transfer and texture synthesis, we propose a new theoretical framework to construct visual metamers: textit{a family of perceptually identical, yet physically different images}. We review work both in neuroscience related to metameric stimuli, as well as computer vision research in style transfer. We propose our NeuroFovea metamer model that is based on a mixture of peripheral representations and style transfer forward-pass algorithms for emph{any} image from the recent work of Adaptive Instance Normalization (Huang~u0026~Belongie). Our model is parametrized by a VGG-Net versus a set of joint statistics of complex wavelet coefficients which allows us to encode images in high dimensional space and interpolate between the content and texture information. We empirically show that human observers discriminate our metamers at a similar rate as the metamers of Freeman~u0026~Simoncelli (FS) In addition, our NeuroFovea metamer model gives us the benefit of near real-time generation which presents a $times1000$ speed-up compared to previous work. Critically, psychophysical studies show that both the FS and NeuroFovea metamers are discriminable from the original images highlighting an important limitation of current metamer generation methods.