基于膨胀腐蚀卷积块的单幅图像去雨算法

成像设备在雨天拍摄图像时由于雨雾和雨条纹的存在会导致图像质量严重退化,对后续图像处理性能造成极大影响.因此,图像的去雨算法研究引起了广泛关注,其中针对单幅图像的去雨算法由于没有先验知识的支持,面临较大挑战.近年来,深度学习因其高特征表示能力被应用在图像去雨算法研究中.本文基于小波变换,采取了一种深度学习与数字图像形态学处理相结合的算法来实现单幅图像去雨,具有训练参数少、训练时间短和去雨效果好等优点.首先对含雨图像进行小波变换,分为低频分量、水平高频分量、垂直高频分量和对角高频分量,然后对这4个分量分别构造深度学习神经网络,并在神经网络架构中根据雨的特征加入图像膨胀、腐蚀等形态学处理来进行去雨操作,大大简化了模型架构,并能取得较好的结果.

Rain Removal Algorithm for Single Image Based on Expansive Corrosion Convolution Block

When imaging equipment takes images on rainy days, the image quality will be seriously degraded due to the existence of rain fog and rain stripes, which will greatly affect the subsequent image processing performance. Therefore, the research on image rain removal algorithms has attracted wide attention, and the rain removal algorithm for a single image is facing great challenges because it is not supported by prior knowledge. In recent years, deep learning has been applied to the research on image rain removal algorithms because of its high feature representation ability. In this study, based on wavelet transform, an algorithm combining deep learning with morphological processing of digital images is adopted to remove rain from a single image, which has the advantages of a few training parameters, short training time, and good rain removal effects. Firstly, the image containing rain is decomposed into a low-frequency component, horizontal high-frequency component, vertical high-frequency component, and diagonal high-frequency component by wavelet transform. Then, the four components are constructed into deep learning neural networks respectively, and morphological processing such as image expansion and corrosion is added to the neural network architecture according to the rain features to remove rain, which greatly simplifies the model architecture and can achieve good results.