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Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras
April 12, 2024, 4:46 a.m. | Rapha\"el Achddou, Yann Gousseau, Sa\"id Ladjal
cs.CV updates on arXiv.org arxiv.org
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
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