Super Resolution Method for Power Equipment Infrared Imaging Based on Gradient Norm-ratio Prior

Infrared images play an important role in the condition monitoring and fault identification of power equipment. Aiming at solving the problems of low resolution and low definition in the application of infrared images, this paper proposes a super-resolution method for compressed infrared images of sensing power equipment based on the prior constraint of the image gradient ratio (GNR). The GNR prior was introduced into the traditional compressed sensing super-resolution model by analyzing the variation in high-frequency information of power equipment infrared images at different sampling ratios. An effective algorithm was designed to solve the improved model. By introducing auxiliary variables into the semi-quadratic splitting method, different variables were iteratively and alternately solved to realize the super-resolution reconstruction of infrared images. The simulation results show that the introduction of GNR prior information was conducive to the super-resolution algorithm achieving better reconstruction. Compared with existing classical super-resolution methods, the proposed method improves both the subjective visual effect and objective evaluation index.