论文标题
使用背面投影金字塔网络为各种雾霾场景的单图像除泽
Single image dehazing for a variety of haze scenarios using back projected pyramid network
论文作者
论文摘要
学会播放单个朦胧的图像，尤其是使用小型培训数据集非常具有挑战性。我们为这个问题提出了一种新颖的生成对抗网络体系结构，即投射的金字塔网络（BPPNET），可为各种具有挑战性的雾霾条件（包括浓密的雾霾和不均匀的雾兹）提供良好的性能。我们的体系结构结合了多个复杂性的学习，同时通过迭代的UNET块保留空间环境，并通过新型的金字塔卷积块来保留多个尺度的结构信息。这些块一起用于发电机，可以通过背部投影学习。我们已经证明，我们的网络可以接受训练而无需过多使用20张朦胧和非颤抖图像的图像。我们报告了NTIRE 2018上的最先进的表演，用于室内和室外图像，NTIRE 2019 DENSEHAZE DATASET和NTIRE 2020年非均匀雾度数据集。
Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks together for the generator and are amenable to learning through back projection. We have shown that our network can be trained without over-fitting using as few as 20 image pairs of hazy and non-hazy images. We report the state of the art performances on NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE 2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset.
