基于改进CNN-RANSAC的水下图像特征配准方法

An underwater image feature registration method based on improved CNN-RANSAC

水对光的吸收和散射效应降低了水下图像的质量,水下图像的可视范围受到限制,复杂水下场景下的鲁棒性和精确性问题使得特征提取与匹配成为一项具有挑战性的任务。为了更好地配准水下图像,提出了一种改进CNN-RANSAC的水下图像特征配准方法,首先通过基于深度卷积神经网络的水下图像增强方法对水下图像进行增强预处理,通过水下图像分类数据集迁移学习训练VGGNet-16网络框架,利用修改后的网络框架进行特征提取,生成鲁棒的多尺度特征描述符与特征点,经过特征粗匹配与动态内点选择,使用改进的RANSAC方法剔除误匹配点。在大量水下图像数据集上进行了充分的特征提取和特征匹配实验,与基于SIFT和SURF的配准方法相比,该方法能够检测到更多的特征点,实现了匹配正确率的大幅度提高。

Due to the light absorption and scattering effect underwater,the quality of underwater images is reduced,which greatly limits the visual range of underwater images.The robustness and accuracy of complex underwater scenes make feature extraction and matching a challenging task.In order to get better registration of underwater images,this paper proposes an underwater image feature registration method based on improved CNN-RANSAC.Firstly,the underwater image is enhanced by the image enhancement algorithm based on deep convolutional neural network,and the VGGNet-16 framework is trained by transfer learning through the underwater image classification datasets,and the improved framework is applied to carry out feature extraction and generate robust multi-scale characteristic descriptor and feature points.After coarse feature matching and dynamic interior point selection,the improved RANSAC algorithm is used to eliminate false matching points.The feature extraction and feature matching experiments are carried out on a large number of underwater image datasets.Compared with the traditional SIFT and SURF registration algorithms,the proposed algorithm can detect more feature points and achieve a significant improvement in matching accuracy.