Moving Image Information-fusion-analysis Algorithm based on Multi-sensor

The image information captured by a sensor in a network environment shows diversity and uncertainty, and it is difficult to achieve good data information processing and fusion because of the difference in characteristics of multiple images collected without time and space, which has caused considerable interference to the authenticity of the image. A multi-sensor-based information fusion analysis algorithm for moving images is proposed to improve the visual effects of image fusion and the signal-to-noise ratio and information entropy. The convolutional neural network (CNN) is used to extract the features of moving images. The mixed function control curve method generates the time series of moving images. According to the time series of the moving image obtained, the moving image is decomposed by a wavelet. A color space model (CSM) is established, and image fusion and optimization are realized using the multi-sensor fusion and multi-objective particle swarm optimization (PSO) algorithm. The proposed method significantly improved the SNR value and information entropy and reduced the standard mean square error. In addition, it had a remarkable image fusion visual effect. © 2023 Institute of Electronics and Information Engineers. All rights reserved.