In this work, we propose a learning-based method to denoise and refine
disparity maps of a given stereo method. The proposed variational network
arises naturally from unrolling the iterates of a proximal gradient method
applied to a variational energy defined in a joint disparity, color, and
confidence image space. Our method allows to learn a robust collaborative
regularizer leveraging the joint statistics of the color image, the confidence
map and the disparity map. Due to the variational structure of our method, the
individual steps can be easily visualized, thus enabling interpretability of
the method. We can therefore provide interesting insights into how our method
refines and denoises disparity maps. The efficiency of our method is
demonstrated by the publicly available stereo benchmarks Middlebury 2014 and
Kitti 2015.

本文提出了一种基于学习的方法来去噪和优化所给定的立体方法的视差图。该方法通过资源一个由联合视差、颜色和置信度图像空间中的变分能量定义的近端梯度方法的迭代来自然生成。我们的方法允许学习鲁棒的协作正则化器，利用颜色图像、置信度图和视差图的联合统计信息。由于我们的方法是变分结构的，因此可以轻松可视化单个步骤，从而实现可解释性。因此，我们可以提供如何优化和去噪视差图的有趣见解。我们的方法的效率得到了公开可用的 Middlebury 2014 和 Kitti 2015 立体基准测试的证明。

学习的协作立体修复

Learned Collaborative Stereo Refinement

Purpose: To allow fast and high-quality reconstruction of clinical
accelerated multi-coil MR data by learning a variational network that combines
the mathematical structure of variational models with deep learning.
Theory and Methods: Generalized compressed sensing reconstruction formulated
as a variational model is embedded in an unrolled gradient descent scheme. All
parameters of this formulation, including the prior model defined by filter
kernels and activation functions as well as the data term weights, are learned
during an offline training procedure. The learned model can then be applied
online to previously unseen data.
Results: The variational network approach is evaluated on a clinical knee
imaging protocol. The variational network reconstructions outperform standard
reconstruction algorithms in terms of image quality and residual artifacts for
all tested acceleration factors and sampling patterns.
Conclusion: Variational network reconstructions preserve the natural
appearance of MR images as well as pathologies that were not included in the
training data set. Due to its high computational performance, i.e.,
reconstruction time of 193 ms on a single graphics card, and the omission of
parameter tuning once the network is trained, this new approach to image
reconstruction can easily be integrated into clinical workflow.

利用数学变分模型与深度学习相结合的可变网络，快速高质量重建临床加速多线圈 MR 数据，从而提高图像质量和去除残留伪影，保留 MRI 图像的自然外观和病理特征，可有效地整合到临床工作流中。