Research on Multi-Modal Artificial Intelligence Information Fusion Technology in Embedded Computer Systems

Multi-scale decomposition (MSD) has some problems, such as missing details and generating noise. This project aims to study multi-modal image based on texture decomposition model and using improved convolutional network and phase-shift consistency techniques. Aiming at the problem that MSD is easy to produce noise, low-pass filter optimization function and structure texture decomposition model are introduced to effectively solve the problem of decomposition technology. An adaptive filtering algorithm based on least square method is proposed, and the structure-texture analysis method based on image is combined to overcome the noise problem of traditional algorithms theoretically. Secondly, for structured texture, the fusion algorithm of convolutional neural network and Gaussian fairing is studied to enhance the effective acquisition of image details and remove noise. The phase-shift matching algorithm is used for high frequency band to achieve effective fusion of high frequency signals. Then the inversion is carried out to obtain the final composite image. The images are studied qualitatively and quantitatively from the perspectives of visual effect, mutual information, feature mutual information, structural similarity, information entropy, PSNR, etc.