An image can be decomposed into two parts: the basic content and details, which usually correspond to the low-frequency and high-frequency information of the image. For a hazy image, these two parts are often affected by haze in different levels, e.g. , high-frequency parts are often affected more serious than low-frequency parts. In this paper, we approach the single image dehazing problem as two restoration problems of recovering basic content and image details, and propose a Dual-Path Recurrent Network (DPRN) to simultaneously tackle these two problems. Specifically, the core structure of DPRN is a dual-path block, which uses two parallel branches to learn the characteristics of the basic content and details of hazy images. Each branch consists of several Convolutional LSTM blocks and convolution layers. Moreover, a parallel interaction function is incorporated into the dual-path block, thus enables each branch to dynamically fuse the intermediate features of both the basic content and image details. In this way, both branches can benefit from each other, and recover the basic content and image details alternately, therefore alleviating the color distortion problem in the dehazing process. Experimental results show that the proposed DPRN outperforms state-of-the-art image dehazing methods in terms of both quantitative accuracy and qualitative visual effect.

中文翻译：

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通过双路径循环网络进行单图像去雾


图像可以分解为基本内容和细节两部分，通常对应图像的低频和高频信息。对于有雾的图像，这两个部分往往受到不同程度的雾霾影响，例如，高频部分往往比低频部分受到的影响更严重。在本文中，我们将单图像去雾问题视为恢复基本内容和图像细节的两个恢复问题，并提出了一种双路径循环网络（DPRN）来同时解决这两个问题。具体来说，DPRN的核心结构是双路径块，它使用两个并行的分支来学习有雾图像的基本内容和细节的特征。每个分支由多个卷积 LSTM 块和卷积层组成。此外，双路径块中加入了并行交互功能，从而使每个分支能够动态融合基本内容和图像细节的中间特征。这样，两个分支可以互相受益，交替恢复基本内容和图像细节，从而缓解去雾过程中的颜色失真问题。实验结果表明，所提出的 DPRN 在定量精度和定性视觉效果方面都优于最先进的图像去雾方法。