Image-to-image translation based on generative adversarial network (GAN) has
achieved state-of-the-art performance in various image restoration
applications. Single image dehazing is a typical example, which aims to obtain
the haze-free image of a haze one. This paper concentrates on the challenging
task of single image dehazing. Based on the atmospheric scattering model, a
novel model is designed to directly generate the haze-free image. The main
challenge of image dehazing is that the atmospheric scattering model has two
parameters, i.e., transmission map and atmospheric light. When they are
estimated respectively, the errors will be accumulated to compromise the
dehazing quality. Considering this reason and various image sizes, a novel
input-size flexibility conditional generative adversarial network (cGAN) is
proposed for single image dehazing, which is input-size flexibility at both
training and test stages for image-to-image translation with cGAN framework. A
simple and effective U-connection residual network (UR-Net) is proposed to
combine the generator and adopt the spatial pyramid pooling (SPP) to design the
discriminator. Moreover, the model is trained with multi-loss function, in
which the consistency loss is a novel designed loss in this paper. Finally, a
multi-scale cGAN fusion model is built to realize state-of-the-art single image
dehazing performance. The proposed models receive a haze image as input and
directly output a haze-free one. Experimental results demonstrate the
effectiveness and efficiency of the proposed models.

本文提出了一种基于生成对抗网络的图像翻译方法，用于单幅图像去雾。该方法主要是采用了新的凝视 - 命题残差网络，以及基于多损失函数的条件生成对抗网络。实验结果表明该方法能够有效地去除图片中的雾霾，并具有较高的效率。

一种基于 GAN 的单幅图像去雾输入尺寸灵活性模型

A GAN-Based Input-Size Flexibility Model for Single Image Dehazing

We present a robust real-time LiDAR 3D object detector that leverages
heteroscedastic aleatoric uncertainties to significantly improve its detection
performance. A multi-loss function is designed to incorporate uncertainty
estimations predicted by auxiliary output layers. Using our proposed method,
the network ignores to train from noisy samples, and focuses more on
informative ones. We validate our method on the KITTI object detection
benchmark. Our method surpasses the baseline method which does not explicitly
estimate uncertainties by up to nearly 9% in terms of Average Precision (AP).
It also produces state-of-the-art results compared to other methods while
running with an inference time of only 72 ms. In addition, we conduct extensive
experiments to understand how aleatoric uncertainties behave. Extracting
aleatoric uncertainties brings almost no additional computation cost during the
deployment, making our method highly desirable for autonomous driving
applications.

该研究提出了利用异方差不确定性的鲁棒的实时 LiDAR 3D 物体检测器，其多损失函数设计了不确定性预测，使网络忽略嘈杂的样本并专注于信息性的样本，超过基线方法近 9％的平均精度。此外，该方法在只需要 72 毫秒的推理时间内，在 KITTI 物体检测基准上产生了最先进的结果，对于自动驾驶应用非常理想。