UVA Image Registration Model Based on VGG and Multi-Branch Attention

Abstract

For UVA images with different resolutions and large areas of weak texture, image feature extraction is insufficient and mis-matching is increased during image registration. To solve these problems, an unsupervised registration model based on VGG feature extraction and multi-branch attention is proposed. First of all, two feature extraction networks with shared weight parameters are used to extract the low and high level fusion features of the moving image and the reference image. The convolution neural network is used to extract the high-dimensional feature map of the image, and the key points are selected according to the conditions that meet both the channel maximum and the local maximum, and the corresponding 512-dimensional descriptor is extracted on the feature map, In the matching stage, add multi-branch attention based on residual block to filter out the wrong features. The algorithm is tested with multiple groups of images and compared with several image matching algorithms. The results show that the algorithm can extract the scale-invariant similar features of images, and has strong adaptability and robustness.