论文标题
Urie：野外视觉识别的通用图像增强
URIE: Universal Image Enhancement for Visual Recognition in the Wild
论文作者
论文摘要
尽管视觉识别方面取得了巨大进步，但已经看到，在公共数据集的干净图像上训练的识别模型对现实世界中的扭曲图像并不强大。为了解决这个问题，我们提出了一个通用且识别的图像增强网络，称为Urie，该网络附加在现有识别模型前，并增强了扭曲的输入，以提高其性能而无需重新训练。 Urie是普遍的，因为它旨在处理图像降解的各种因素，并将其与任何任意识别模型合并。同样，它非常适合识别，因为它被优化以提高以下识别模型的鲁棒性，而不是输出图像的感知质量。我们的实验表明，当输入图像降低时，URIE可以处理各种和潜在的图像扭曲，并改善五种不同识别任务的现有模型的性能。
Despite the great advances in visual recognition, it has been witnessed that recognition models trained on clean images of common datasets are not robust against distorted images in the real world. To tackle this issue, we present a Universal and Recognition-friendly Image Enhancement network, dubbed URIE, which is attached in front of existing recognition models and enhances distorted input to improve their performance without retraining them. URIE is universal in that it aims to handle various factors of image degradation and to be incorporated with any arbitrary recognition models. Also, it is recognition-friendly since it is optimized to improve the robustness of following recognition models, instead of perceptual quality of output image. Our experiments demonstrate that URIE can handle various and latent image distortions and improve the performance of existing models for five diverse recognition tasks when input images are degraded.
