Reconstructing Group Wavelet Transform From Feature Maps With a Reproducing Kernel Iteration
Davide Francesco Barbieri · Frontiers in Computational Neuroscience · 2022
In this article, we consider the problem of reconstructing an image that is downsampled in the space of its SE(2) wavelet transform, which is motivated by classical models of simple cell receptive fields and feature preference maps in the primary visual cortex. We prove that, whenever the problem is solvable, the reconstruction can be obtained by an elementary project and replace iterative scheme based on the reproducing kernel arising from the group structure, and show numerical results on real images.