Blind Super Resolution for Infrared Image of Power Equipment Based on Compressed Sensing

The resolution reduction and image blur in the process of infrared image acquisition are important factors affecting the accuracy of infrared diagnosis. However, the existing super-resolution methods to solve this problem are usually to assume that the fuzzy kernel is known, but when the assumed fuzzy kernel deviates from the real core, its performance will decrease significantly. In this paper, a new method of blind super-resolution compression sensing is proposed. Based on the image degradation model and the prior knowledge of image sparse in the transform domain, the super-resolution reconstruction from low-resolution image to high-resolution image is realized. In the reconstruction process, the optimal solution of fuzzy kernel and deconvolution of reconstructed image are promoted by the prior information of the extreme distribution of brightness components of the infrared image. Hence, the traditional non-blind super-resolution method is improved to the blind super-resolution method. The accuracy of the model is further improved, and the texture quality of the reconstructed image details is also improved. The compression sensing model is perfected and the high-quality reconstruction of high-resolution image is realized, which can meet the needs of engineering application. The experimental results show that the blind super-resolution method proposed in this paper can better meet the needs of the actual acquisition of the low-resolution infrared image super-resolution reconstruction. The results of the reconstruction have some advantages in both subjective vision and objective evaluation indexes. © 2022, Power System Technology Press. All right reserved.