Application of deep learning model based on image definition in real-time digital image fusion

摘要

This paper focuses on pulse coupled neural network (PCNN) and digital image fusion. Aiming at the existing problems, this paper proposes a real-time deep learning model with dual-channel PCNN fusion algorithm based on image definition. It will also be helpful to digital image forensics. With the integration of the orthogonal color space that conforms to HVS, this algorithm simplifies the traditional PCNN model to a parallel dual-channel adaptive PCNN structure. Also, it can realize the adaptive processing by defining the image definition to be beta, the coupled linking coefficient. As the dynamic threshold can be increased exponentially with this method, it can effectively solve the problems. The experimental result proves that our algorithm outperforms the traditional fusion algorithms according to the subjective visual effect or the objective assessment standard.