论文标题
夜间脱胸增强
Nighttime Dehaze-Enhancement
论文作者
论文摘要
在本文中，我们介绍了一项新的计算机视觉任务，称为夜间脱气增强。这项任务旨在共同执行飞行和轻便增强。我们的任务从根本上不同于夜间飞行 - 我们的目标是共同脱颖而出和增强场景，而夜间飞机的旨在在夜间环境下脱颖而出。为了促进有关此任务的进一步研究，我们发布了一个名为“ Reside-$β$ night数据集”的新基准数据集，该数据集由2061个场景和2061个地面真相图像的4122个夜间危险图像组成。此外，我们还提出了一个名为Ndenet（Night Time Dehaze-Enhahancement网络）的新网络，该网络以端到端的方式共同执行脱掩护和低光增强。我们在提出的基准上评估我们的方法，并将SSIM达到0.8962，PSNR为26.25。我们还将我们的网络与基准基准上的其他基线网络进行了比较，以证明我们的方法的有效性。我们认为，夜间的脱胸大增强是一项重要的任务，特别是对于自动导航应用程序，并希望我们的工作能为研究开辟新的边界。我们的数据集和代码将在接受我们的论文后公开提供。
In this paper, we introduce a new computer vision task called nighttime dehaze-enhancement. This task aims to jointly perform dehazing and lightness enhancement. Our task fundamentally differs from nighttime dehazing -- our goal is to jointly dehaze and enhance scenes, while nighttime dehazing aims to dehaze scenes under a nighttime setting. In order to facilitate further research on this task, we release a new benchmark dataset called Reside-$β$ Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and 2061 ground truth images. Moreover, we also propose a new network called NDENet (Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and low-light enhancement in an end-to-end manner. We evaluate our method on the proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also compare our network with other baseline networks on our benchmark to demonstrate the effectiveness of our approach. We believe that nighttime dehaze-enhancement is an essential task particularly for autonomous navigation applications, and hope that our work will open up new frontiers in research. Our dataset and code will be made publicly available upon acceptance of our paper.
