TY - GEN
T1 - Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras
AU - Achddou, Raphaël
AU - Gousseau, Yann
AU - Ladjal, Saïd
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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. The feasibility of the approach is demonstrated both on the denoising of RGB images and the full development of RAW images, opening the path to a systematic improvement of the texture acutance of real imaging devices.
AB - 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. The feasibility of the approach is demonstrated both on the denoising of RGB images and the full development of RAW images, opening the path to a systematic improvement of the texture acutance of real imaging devices.
KW - Deep learning
KW - Image denoising
KW - Image quality assessment
UR - https://www.scopus.com/pages/publications/85161213095
U2 - 10.1007/978-3-031-31975-4_24
DO - 10.1007/978-3-031-31975-4_24
M3 - Conference contribution
AN - SCOPUS:85161213095
SN - 9783031319747
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 314
EP - 325
BT - Scale Space and Variational Methods in Computer Vision - 9th International Conference, SSVM 2023, Proceedings
A2 - Calatroni, Luca
A2 - Donatelli, Marco
A2 - Morigi, Serena
A2 - Prato, Marco
A2 - Santacesaria, Matteo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023
Y2 - 21 May 2023 through 25 May 2023
ER -