Deep learning-based methods have shown remarkable success for various image
restoration tasks such as denoising and deblurring. The current
state-of-the-art networks are relatively deep and utilize (variants of) self
attention mechanisms. Those networks are significantly slower than shallow
convolutional networks, which however perform worse. In this paper, we
introduce an image restoration network that is both fast and yields excellent
image quality. The network is designed to minimize the latency and memory
consumption when executed on a standard GPU, while maintaining state-of-the-art
performance. The network is a simple shallow network with an efficient block
that implements global additive multidimensional averaging operations. This
block can capture global information and enable a large receptive field even
when used in shallow networks with minimal computational overhead. Through
extensive experiments and evaluations on diverse tasks, we demonstrate that our
network achieves comparable or even superior results to existing
state-of-the-art image restoration networks with less latency. For instance, we
exceed the state-of-the-art result on real-world SIDD denoising by 0.11dB,
while being 2 to 10 times faster.

通过使用浅层网络和高效的块实现全局加性多维均值操作，我们介绍了一种图像复原网络，既具有快速执行速度又能提供优秀的图像质量。通过广泛的实验证明，我们的网络在各种任务上实现了与现有的最先进图像复原网络相媲美甚至更优秀的结果，并且具有较低的延迟。

GAMA-IR：全球累加多维均值用于快速图像恢复

GAMA-IR: Global Additive Multidimensional Averaging for Fast Image  Restoration

Reliable obstacle detection on railways could help prevent collisions that
result in injuries and potentially damage or derail the train. Unfortunately,
generic object detectors do not have enough classes to account for all possible
scenarios, and datasets featuring objects on railways are challenging to
obtain. We propose utilizing a shallow network to learn railway segmentation
from normal railway images. The limited receptive field of the network prevents
overconfident predictions and allows the network to focus on the locally very
distinct and repetitive patterns of the railway environment. Additionally, we
explore the controlled inclusion of global information by learning to
hallucinate obstacle-free images. We evaluate our method on a custom dataset
featuring railway images with artificially augmented obstacles. Our proposed
method outperforms other learning-based baseline methods.

铁路上可靠的障碍物检测可以帮助防止导致伤害、潜在的损坏或脱轨的碰撞。我们提出利用浅层网络从正常的铁路图像中学习铁路分割，并通过学习虚构无障碍图像的全局信息的受控包含来改进方法。我们在一个包含人工增强障碍物的铁路图像数据集上评估我们的方法，结果表明我们的方法优于其他基于学习的基准方法。

铁路轨道上的障碍物检测中的本地和全局信息

Local and Global Information in Obstacle Detection on Railway Tracks

An additive autoencoder for dimension reduction, which is composed of a
serially performed bias estimation, linear trend estimation, and nonlinear
residual estimation, is proposed and analyzed. Computational experiments
confirm that an autoencoder of this form, with only a shallow network to
encapsulate the nonlinear behavior, is able to identify an intrinsic dimension
of a dataset with a low autoencoding error. This observation leads to an
investigation in which shallow and deep network structures, and how they are
trained, are compared. We conclude that the deeper network structures obtain
lower autoencoding errors during the identification of the intrinsic dimension.
However, the detected dimension does not change compared to a shallow network.

本文针对降维问题提出并分析了一种由序列化偏差估计、线性趋势估计和非线性残差估计组成的增量自编码器。实验结果表明，仅通过浅层网络封装非线性行为的自编码器能够识别具有低自编码误差的数据集的内在维度。该研究进一步比较了深层和浅层网络结构及其训练方法，发现深层网络结构在识别内在维度时获得较低的自编码误差，但是与浅层网络相比，检测到的维度并没有变化。