论文标题
半u孔：半监督不确定性的不确定性变压器，用于向上的图像
Semi-UFormer: Semi-supervised Uncertainty-aware Transformer for Image Dehazing
论文作者
论文摘要
图像飞机是基本的，但在计算机视觉中却没有很好地解决。大多数尖端模型都接受了合成数据的训练，从而导致现实世界中的朦胧场景的性能不佳。此外，他们通常会提供确定性的降落图像，同时忽略挖掘自己的不确定性。为了弥合域间隙并增强了飞行性能，我们提出了一种新型的半监督不确定性感知的变压器网络，称为Semi-Uformer。半U形式可以很好地利用现实世界中的朦胧图像及其不确定性指导信息。具体而言，半us形成在知识蒸馏框架上的建立。这种教师学生的网络有效地吸收了现实世界中的雾霾信息，以进行质量飞扬。此外，将不确定性估计块引入模型中，以估计像素不确定性表示，然后将其用作指导信号，以帮助学生网络更准确地生成无雾的图像。广泛的实验表明，半增长器从合成到现实世界图像良好概括。
Image dehazing is fundamental yet not well-solved in computer vision. Most cutting-edge models are trained in synthetic data, leading to the poor performance on real-world hazy scenarios. Besides, they commonly give deterministic dehazed images while neglecting to mine their uncertainty. To bridge the domain gap and enhance the dehazing performance, we propose a novel semi-supervised uncertainty-aware transformer network, called Semi-UFormer. Semi-UFormer can well leverage both the real-world hazy images and their uncertainty guidance information. Specifically, Semi-UFormer builds itself on the knowledge distillation framework. Such teacher-student networks effectively absorb real-world haze information for quality dehazing. Furthermore, an uncertainty estimation block is introduced into the model to estimate the pixel uncertainty representations, which is then used as a guidance signal to help the student network produce haze-free images more accurately. Extensive experiments demonstrate that Semi-UFormer generalizes well from synthetic to real-world images.
