融入注意力机制的多尺度卷积图像去雾方法

An image dehazing method based on multi-scale convolution with attention mechanism

图像的去雾问题是一项富有挑战性的视觉任务。以往的图像去雾方法往往过于依赖雾天图像退化的物理模型,且当前利用卷积神经网络进行图像去雾的模型较为复杂,基于此提出一种不依赖于物理模型的轻量级去雾网络MADNet。该网络主要由融入注意力机制的多尺度卷积模块构成,通过将有雾图像看成是清晰的无雾图像和雾度残留图像组成,让MADNet直接学习目标无雾图像和输入的有雾图像之间的雾度残留物,最后实现端到端的图像去雾。实验结果表明,MADNet在数据集SOTS和NH-HAZE上的结构相似性和峰值性噪比均优于其它对比方法的,在真实场景中也能取得较好的去雾效果。

Image dehazing is a challenging visual task.Previous image dehazing method often depend too much on the physical model of images degraded by fog,and the current image dehazing model using convolution neural network is more complex.Therefore,a lightweight dehazing network MADNet that does not depend on physical model is proposed.The network is mainly composed of a multi-scale convolution module with attention mechanism.By viewing foggy images as composed of clear images and fog residue images,MADNet directly learns the fog residue between the target clear image and the input foggy image,and finally achieve end-to-end image fog removal.The experimental results show that the structure similarity and peak signal-to-noise ratio of the proposed method are better than those of other comparison method on SOTS and NH-HAZE datasets,and it can also achieve better fog removal in real scenes.