摘要
使用单一卷积神经网络去雾算法容易存在对比度偏低、细节信息丢失和去雾不完全等缺陷。为了解决上述问题,提出了一种增强型金字塔模型和图像超分辨率并联去雾网络结构。增强机制作用于特征金字塔图像重建过程,用以提升去雾图像信噪比。通道注意力将编码器提取的特征信息映射到解码器,赋予每个通道不同权重,以此提高去雾效率。超分辨率网络补充更多高频特征细节,提升去雾图像的清晰度。实验证明,增强型金字塔及超分辨率网络具有较强的去雾能力,性能优于其他方法,有效抑制单一的卷积神经网络输出图像分辨率下降问题。
Using a single convolutional neural network dehazing algorithm is prone to low contrast,loss of detail information and incomplete dehazing.In order to solve the above problems,a boosted pyramid model and image super-resolution parallel demisting network structure are proposed.The boost algorithm acts on the feature pyramid image reconstruction process to improve the signal-to-noise ratio of the defogging image.Channel attention maps the feature information extracted by the encoder to the decoder,giving each channel different weights,so as to improve the efficiency of dehazing.The super-resolution network adds more high-frequency feature details to improve the clarity of the dehazing image.Experiments show that the boosted pyramid and super-resolution network have strong dehazing ability,and their performance is better than other methods,which can effectively suppress the degradation of the output image resolution of a single convolutional neural network.
作者
王科平
肖梦临
WANG Keping;XIAO Menglin(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,China)
出处
《兵器装备工程学报》
CAS
CSCD
北大核心
2023年第11期299-307,共9页
Journal of Ordnance Equipment Engineering
关键词
图像去雾
增强机制
超分辨率
特征金字塔
image dehazing
boosted algorithm
super-resolution
feature pyramid
