Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras

arXiv:2404.07212v1 Announce Type: cross
Abstract: In order to evaluate the capacity of a camera to render textures properly, the standard practice, used by classical scoring protocols, is to compute the frequential response to a dead leaves image target, from which is built a texture acutance metric. In this work, we propose a mixed training procedure for image restoration neural networks, relying on both natural and synthetic images, that yields a strong improvement of this acutance metric without impairing fidelity terms. …

abstract arxiv cameras capacity compute cs.ai cs.cv denoising digital eess.iv hybrid image networks practice scoring standard texture training type