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.

通过使用自然图像和合成图像的混合训练程序，本研究提出了一种对于图像修复神经网络的训练方法，从而在不损害保真度的前提下显著改善纹理清晰度度量标准，证实了这种方法在消噪 RGB 图像和完全开发 RAW 图像方面的可行性，为实际成像设备的纹理清晰度系统改进开辟了道路。

数字相机纹理锐度的混合训练去噪网络改进

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

Stereo matching is an important problem in computer vision which has drawn
tremendous research attention for decades. Recent years, data-driven methods
with convolutional neural networks (CNNs) are continuously pushing stereo
matching to new heights. However, data-driven methods require large amount of
training data, which is not an easy task for real stereo data due to the
annotation difficulties of per-pixel ground-truth disparity. Though synthetic
dataset is proposed to fill the gaps of large data demand, the fine-tuning on
real dataset is still needed due to the domain variances between synthetic data
and real data. In this paper, we found that in synthetic datasets,
close-to-real-scene texture rendering is a key factor to boost up stereo
matching performance, while close-to-real-scene 3D modeling is less important.
We then propose semi-synthetic, an effective and fast way to synthesize large
amount of data with close-to-real-scene texture to minimize the gap between
synthetic data and real data. Extensive experiments demonstrate that models
trained with our proposed semi-synthetic datasets achieve significantly better
performance than with general synthetic datasets, especially on real data
benchmarks with limited training data. With further fine-tuning on the real
dataset, we also achieve SOTA performance on Middlebury and competitive results
on KITTI and ETH3D datasets.

本文提出半合成数据集的方法，通过接近真实场景纹理的渲染方式，以减少合成数据集与真实数据集之间的差距，进而提高立体匹配性能，此方法在 Middlebury 数据集上取得了 SOTA 的成绩，在 KITTI 和 ETH3D 数据集上也取得了竞争性的结果。