A sub-Riemannian model of the visual cortex with frequency and phase
This work addresses the challenge of accurately simulating V1 neural connectivity for computational neuroscience and image processing, though it appears incremental as it builds on existing Gabor-based models.
The authors tackled the problem of modeling the primary visual cortex (V1) by incorporating orientation, frequency, and phase selectivity, resulting in a novel geometric model and an image enhancement algorithm that leverages these features.
In this paper we present a novel model of the primary visual cortex (V1) based on orientation, frequency and phase selective behavior of the V1 simple cells. We start from the first level mechanisms of visual perception: receptive profiles. The model interprets V1 as a fiber bundle over the 2-dimensional retinal plane by introducing orientation, frequency and phase as intrinsic variables. Each receptive profile on the fiber is mathematically interpreted as a rotated, frequency modulated and phase shifted Gabor function. We start from the Gabor function and show that it induces in a natural way the model geometry and the associated horizontal connectivity modeling the neural connectivity patterns in V1. We provide an image enhancement algorithm employing the model framework. The algorithm is capable of exploiting not only orientation but also frequency and phase information existing intrinsically in a 2-dimensional input image. We provide the experimental results corresponding to the enhancement algorithm.