基于生成对抗网络的图像去雾算法

与基于图像增强的去雾算法和基于物理模型的去雾算法相比,基于深度学习的图像去雾方法在一定程度上提高计算效率,但在场景复杂时仍存在去雾不彻底及颜色扭曲的问题.针对人眼对全局特征和局部特征的感受不同这一特性,文中构建基于生成对抗网络的图像去雾算法.首先设计多尺度结构的生成器网络,分别以全尺寸图像和分割后的图像块作为输入,提取图像的全局轮廓信息和局部细节信息.然后设计一个特征融合模块,融合全局信息和局部信息,通过判别网络判断生成无雾图像的真假.为了使生成的去雾图像更接近对应的真实无雾图像,设计多元联合损失函数,结合暗通道先验损失函数、对抗损失函数、结构相似性损失函数及平滑L1损失函数训练网络.在合成数据集和真实图像上与多种算法进行实验对比,结果表明,文中算法的去雾效果较优.

Image Dehazing Based on Generative Adversarial Network

Compared with the image dehazing methods based on image enhancement or physical model, the current image dehazing methods based on deep learning improve the computational efficiency to a certain extent. Nevertheless, the problems of incomplete dehazing and color distortion still exist in complex scenes. Aiming at the different perceptions of human eyes on global and local features, an algorithm of image dehazing based on generative adversarial networks is proposed. Firstly, a multi-scale generator network is designed. The full-size image and the segmented image block are taken as the input to extract the global contour information and local detail information of the image. Then, a feature fusion module is constructed to fuse the global and local information, and the authenticity of the generated dehazing image is judged by the discriminant network. To make the generated dehazing image closer to the corresponding real haze-free image, a multivariate joint loss function is designed by combining the dark channel prior loss, the adversarial loss, the structural similarity loss and the smooth L1 loss to train the network. Experimental results show that the proposed algorithm is superior to some state-of-the-art dehazing algorithms.