Compressed Sensing Super-Resolution Method for Improving the Accuracy of Infrared Diagnosis of Power Equipment

Abstract

The infrared image of power equipment plays a crucial role in identifying faults, monitoring equipment condition, and so on. The low resolution and low definition of infrared images in applications contribute to the low accuracy of infrared diagnosis. A super-resolution reconstruction method of infrared image, based on compressed sensing theory, is proposed. Firstly, by analyzing the variation of high-frequency information in infrared images with different blurring degrees, the image gradient norm ratio is introduced to estimate the blur kernel matrix in the degradation model a priori. Then, in the process of image reconstruction, we add the full variational regularization term to the traditional compressed sensing model, and design a two-step full variational sparse reconstruction algorithm. Experimental results verify the effectiveness of the method. Compared with the existing classical super-resolution methods, this method offers improvement in subjective visual effect and objective evaluation index. In addition, the final image recognition and infrared diagnosis experiments show that this method is helpful to improve the accuracy of infrared diagnosis of power equipment.