  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.
